A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization

Accurate location information can offer huge commercial and social value and has become a key research topic. Acoustic-based positioning has high positioning accuracy, although some anomalies that affect the positioning performance arise. Inertia-assisted positioning has excellent autonomous characteristics, but its localization errors accumulate over time. To address these issues, we propose a novel positioning navigation system that integrates acoustic estimation and dead reckoning with a novel step-length model. First, the features that include acceleration peak-to-valley amplitude difference, walk frequency, variance of acceleration, mean acceleration, peak median, and valley median are extracted from the collected motion data. The previous three steps and the maximum and minimum values of the acceleration measurement at the current step are extracted to predict step length. Then, the LASSO regularization spatial constraint under the extracted features optimizes and solves for the accurate step length. The acoustic estimation is determined by a hybrid CHAN-Taylor algorithm. Finally, the location is determined using an extended Kalman filter (EKF) merged with the improved pedestrian dead reckoning (PDR) estimation and acoustic estimation. We conducted some comparative experiments in two different scenarios using two heterogeneous devices. The experimental results show that the proposed fusion positioning navigation method achieves 8~56.28 cm localization accuracy. The proposed method can significantly migrate the cumulative error of PDR and high-robustness localization under different experimental conditions.

Deep Neural Network-Based Fusion Localization Using Smartphones

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.

Context-Aware Statistical Dead Reckoning for Localization in IoT Scenarios

The current trends in 5G and 6G systems anticipate vast communication capabilities and the deployment of massive heterogeneous connectivity with more than a million internet of things (IoT) and other devices per square kilometer and up to ten million gadgets in 6G scenarios. In addition, the new generation of smart industries and the energy of things (EoT) context demand novel, reliable, energy-efficient network protocols involving massive sensor cooperation. Such scenarios impose new demands and opportunities to cope with the ever-growing cooperative dense ad hoc environments. Position location information (PLI) plays a crucial role as an enabler of several location-aware network protocols and applications. In this paper, we have proposed a novel context-aware statistical dead reckoning localization technique suitable for high dense cooperative sensor networks, where direct angle and distance estimations between peers are not required along the route, as in other dead reckoning-based localization approaches, but they are obtainable from the node's context information. Validation of the proposed technique was assessed in several scenarios through simulations, achieving localization errors as low as 0.072 m for the worst case analyzed.

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.

A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization

Parameter calibration is critical for self-localization based on dead reckoning in the control of intelligent vehicles such as autonomous driving. Most traditional calibration methods for robotics control based on dead reckoning rely on data collection with specially designed paths. For the calibration of parameters in the control of intelligent vehicles, the design of such paths is considered impossible due to the complexity of road conditions. To solve this problem, an optimization-based dead reckoning calibration scheme is introduced in this research using the differential global positioning system to obtain the actual positions of the intelligent vehicle. In this scheme, the difference between the positions obtained through dead reckoning and the positions obtained through the differential global positioning system is selected as the optimization objective function to be minimized. An adaptive quantum-inspired evolutionary algorithm is developed to improve the quality and efficiency of optimization. Experiments with an intelligent vehicle were also conducted to demonstrate the effectiveness of the developed calibration scheme. In addition, the newly introduced adaptive quantum-inspired evolutionary algorithm is compared with the classic genetic algorithm and the classic quantum-inspired evolutionary algorithm using eight benchmark test functions considering computation quality and efficiency.

Design of a "Cobot Tactile Display" for Accessing Virtual Diagrams by Blind and Visually Impaired Users

Access to graphical information plays a very significant role in today's world. Access to this information can be particularly limiting for individuals who are blind or visually impaired (BVIs). In this work, we present the design of a low-cost, mobile tactile display that also provides robotic assistance/guidance using haptic virtual fixtures in a shared control paradigm to aid in tactile diagram exploration. This work is part of a larger project intended to improve the ability of BVI users to explore tactile graphics on refreshable displays (particularly exploration time and cognitive load) through the use of robotic assistance/guidance. The particular focus of this paper is to share information related to the design and development of an affordable and compact device that may serve as a solution towards this overall goal. The proposed system uses a small omni-wheeled robot base to allow for smooth and unlimited movements in the 2D plane. Sufficient position and orientation accuracy is obtained by using a low-cost dead reckoning approach that combines data from an optical mouse sensor and inertial measurement unit. A low-cost force-sensing system and an admittance control model are used to allow shared control between the Cobot and the user, with the addition of guidance/virtual fixtures to aid in diagram exploration. Preliminary semi-structured interviews, with four blind or visually impaired participants who were allowed to use the Cobot, found that the system was easy to use and potentially useful for exploring virtual diagrams tactually.

Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments

With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. However, there are many cases in real world driving where the satellite signals are challenged; for example, urban streets with buildings, tunnels, or even underpasses. In this paper, we propose a novel method for simultaneous vehicle dead reckoning, based on the lane detection model in GNSS-denied situations. The proposed method fuses the Inertial Navigation System (INS) with learning-based lane detection model to estimate the global position of vehicle, and effectively bounds the error drift compared to standalone INS. The integration of INS and lane model is accomplished by UKF to minimize linearization errors and computing time. The proposed method is evaluated through the real-vehicle experiments on highway driving, and the comparative discussions for other dead-reckoning algorithms with the same system configuration are presented.

A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles

The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning.

Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points.

Feasibility of Location-Aware Handover for Autonomous Vehicles in Industrial Multi-Radio Environments

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.

An Improved Relative GNSS Tracking Method Utilizing Single Frequency Receivers

The Global Navigation Satellite Systems (GNSS) becomes the primary choice for device localization in outdoor situations. At the same time, many applications do not require precise absolute Earth coordinates, but instead, inferring the geometric configuration information of the constituent nodes in the system by relative positioning. The Real-Time Kinematic (RTK) technique shows its efficiency and accuracy in calculating the relative position. However, when the cycle slips occur, the RTK method may take a long time to obtain a fixed ambiguity value, and the positioning result will be a “float” solution with a low meter accuracy. The novel method presented in this paper is based on the Relative GNSS Tracking Algorithm (Regtrack). It calculates the changes in the relative baseline between two receivers without an ambiguity estimation. The dead reckoning method is used to give out the relative baseline solution while a parallel running Extended Kalman Filter (EKF) method reinitiates the relative baseline when too many validation failures happen. We conducted both static and kinematic tests to assess the performance of the new methodology. The experimental results show that the proposed strategy can give accurate millimeter-scale solutions of relative motion vectors in adjacent two epochs. The relative baseline solution can be sub-decimeter level with or without the base station is holding static. In the meantime, when the initial tracking point and base station coordinates are precisely obtained, the tracking result error can be only 40 cm away from the ground truth after a 25 min drive test in an urban environment. The efficiency test shows that the proposed method can be a real-time method, the time that calculates one epoch of measurement data is no more than 80 ms and is less than 10 ms for best results. The novel method can be used as a more robust and accurate ambiguity free tracking approach for outdoor applications.

How Accurate Can UWB and Dead Reckoning Positioning Systems Be? Comparison to SLAM Using the RPLidar System

This paper compares two positioning systems, namely ultra-wideband (UWB) based micro-location technology and dead reckoning and a RPLidar based simultaneous localization and mapping (SLAM) solution. This new approach can be used to improve the quality of the positioning system and increase the functionality of advanced driver assistance systems (ADAS). This is achieved by using stationary nodes and UWB tags on the vehicles. Thus, the redundancy of localization can be achieved by this approach, e.g., as a backup to onboard sensors like RPlidar or radar. Additionally, UWB based micro-location allows additional data channels to be used for communication purposes. Furthermore, it is shown that the regular use of correction data increases UWB and dead reckoning accuracy. These correction data can be based on onboard sensors. This shows that it is promising to develop a system that fuses onboard sensors and micro-localization for safety-critical tasks like the platooning of commercial vehicles.

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

When performing the inspection of subway tunnels, there is an immense amount of data to be collected and the time available for inspection is short; however, the requirement for inspection accuracy is high. In this study, a mobile laser scanning system (MLSS) was used for the inspection of subway tunnels, and the key technology of the positioning and orientation system (POS) was investigated. We utilized the inertial measurement unit (IMU) and the odometer as the core sensors of the POS. The initial attitude of the MLSS was obtained by using a static initial alignment method. Considering that there is no global navigation satellite system (GNSS) signal in a subway, the forward and backward dead reckoning (DR) algorithm was used to calculate the positions and attitudes of the MLSS from any starting point in two directions. While the MLSS passed by the control points distributed on both sides of the track, the local coordinates of the control points were transmitted to the center of the MLSS by using the ranging information of the laser scanner. Then, a four-parameter transformation method was used to correct the error of the POS and transform the 3-D state information of the MLSS from a navigation coordinate system (NCS) to a local coordinate system (LCS). This method can completely eliminate a MLSS's dependence on GNSS signals, and the obtained positioning and attitude information can be used for point cloud data fusion to directly obtain the coordinates in the LCS. In a tunnel of the Beijing-Zhangjiakou high-speed railway, when the distance interval of the control points used for correction was 120 m, the accuracy of the 3-D coordinates of the point clouds was 8 mm, and the experiment also showed that it takes less than 4 h to complete all the inspection work for a 5-6 km long tunnel. Further, the results from the inspection work of Wuhan subway lines showed that when the distance intervals of the control points used for correction were 60 m, 120 m, 240 m, and 480 m, the accuracies of the 3-D coordinates of the point clouds in the local coordinate system were 4 mm, 6 mm, 7 mm, and 8 mm, respectively.

A Novel Cooperative Localization Method Based on IMU and UWB

In this paper, a range-based cooperative localization method is proposed for multiple platforms of various structures. The localization system of an independent platform might degrade or fail due to various reasons such as GPS signal-loss, inertial measurement unit (IMU) accumulative errors, or emergency reboot. It is a promising approach to solve this problem by using information from neighboring platforms, thus forming a cooperative localization network that can improve the navigational robustness of each platform. Typical ranging-based ultra-wideband (UWB) cooperative localization systems require at least three auxiliary nodes to estimate the pose of the target node, which is often hard to meet especially in outdoor environment. In this work, we propose a novel IMU/UWB-based cooperative localization solution, which requires a minimum number of auxiliary nodes that is down to 1. An Adaptive Ant Colony Optimization Particle Filter (AACOPF) algorithm is customized to integrate the dead reckoning (DR) system and auxiliary nodes information with no prior information required, resulting in accurate pose estimation, while to our knowledge the azimuth have not been estimated in cooperative localization for the insufficient observation of the system. We have given the condition when azimuth and localization are solvable by analysis and by experiment. The feasibility of the proposed approach is evaluated through two filed experiments: car-to-trolley and car-to-pedestrian cooperative localization. The comparison results also demonstrate that ACOPF-based integration is better than other filter-based methods such as Extended Kalman Filter (EKF) and traditional Particle Filter (PF).

An Alignment Method for Strapdown Inertial Navigation Systems Assisted by Doppler Radar on a Vehicle-Borne Moving Base

In this study, we investigated a novel method for high-accuracy autonomous alignment of a strapdown inertial navigation system assisted by Doppler radar on a vehicle-borne moving base, which effectively avoids the measurement errors caused by wheel-slip or vehicle-sliding. Using the gyroscopes in a strapdown inertial navigation system and Doppler radar, we calculated the dead reckoning, analyzed the error sources of the dead reckoning system, and established an error model. Then the errors of the strapdown inertial navigation system and dead reckoning system were treated as the states. Besides velocity information, attitude information was cleverly introduced into the alignment measurement to improve alignment accuracy and reduce alignment time. Therefore, the first measurement was the difference between the output attitude and velocity of the strapdown inertial navigation system and the corresponding signals from the dead reckoning system. In order to further improve the alignment accuracy, more measurement information was introduced by using the vehicle motion constraint, that is, the velocity output projection of strapdown inertial navigation system along the transverse and vertical direction of the vehicle body was also used as the second measurement. Then the corresponding state and measurement equations were established, and the Kalman filter algorithm was used for assisted alignment filtering. The simulation results showed that, with a moving base, the misalignment angle estimation accuracy was better than 0.5’ in the east direction, 0.4’ in the north direction, and 3.2’ in the vertical direction.

Robust Stride Detector from Ankle-Mounted Inertial Sensors for Pedestrian Navigation and Activity Recognition with Machine Learning Approaches

In this paper, a stride detector algorithm combined with a technique inspired by zero velocity update (ZUPT) is proposed to reconstruct the trajectory of a pedestrian from an ankle-mounted inertial device. This innovative approach is based on sensor alignment and machine learning. It is able to detect 100% of both normal walking strides and more than 97% of atypical strides such as small steps, side steps, and backward walking that existing methods can hardly detect. This approach is also more robust in critical situations, when for example the wearer is sitting and moving the ankle or when the wearer is bicycling (less than two false detected strides per hour on average). As a consequence, the algorithm proposed for trajectory reconstruction achieves much better performances than existing methods for daily life contexts, in particular in narrow areas such as in a house. The computed stride trajectory contains essential information for recognizing the activity (atypical stride, walking, running, and stairs). For this task, we adopt a machine learning approach based on descriptors of these trajectories, which is shown to be robust to a large of variety of gaits. We tested our algorithm on recordings of healthy adults and children, achieving more than 99% success. The algorithm also achieved more than 97% success in challenging situations recorded by children suffering from movement disorders. Compared to most algorithms in the literature, this original method does not use a fixed-size sliding window but infers this last in an adaptive way.

Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.

A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment

Indoor positioning using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. A number of efforts have been exerted to improve the performance of BLE-based indoor positioning. However, few studies pay attention to the BLE-based indoor positioning in a dense Bluetooth environment, where the propagation of BLE signals become more complex and more fluctuant. In this paper, we draw attention to the problems resulting from the dense Bluetooth environment, and it turns out that the dense Bluetooth environment would result in a high received signal strength indication (RSSI) variation and a longtime interval collection of BLE. Hence, to mitigate the effects of the dense Bluetooth environment, we propose a hybrid method fusing sliding-window filtering, trilateration, dead reckoning and the Kalman filtering method to improve the performance of the BLE indoor positioning. The Kalman filter is exploited to merge the trilateration and dead reckoning. Extensive experiments in a real implementation are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. The implementation results proved that the fusion method was the most effective method to improve the positioning accuracy and timeliness in a dense Bluetooth environment. The positioning root-mean-square error (RMSE) calculation results have showed that the hybrid method can achieve a real-time positioning and reduce error of indoor positioning.

Smartphone Heading Correction Based on Gravity Assisted and Middle Time Simulated-Zero Velocity Update Method

Electronic appliances and ferromagnetic materials can be easily found in any building in urban environment. A steady magnetic environment and a pure value of geomagnetic field for calculating the heading of the smartphone in case of pedestrian walking indoors is hard to obtain. Therefore, an independent inertial heading correction algorithm without involving magnetic field but only making full use of the embedded Micro-Electro-Mechanical System (MEMS) Inertial measurement unit (IMU) device in the smartphone is presented in this paper. Aiming at the strict navigation requirements of pedestrian smartphone positioning, the algorithm focused in this paper consists of Gravity Assisted (GA) and Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) methods. With the help of GA method, the different using-mode of the smartphone can be judged based on the data from the gravity sensor of smartphone. Since there is no zero-velocity status for handheld smartphone, the MTS-ZUPT algorithm is proposed based on the idea of Zero Velocity Update (ZUPT) algorithm. A Kalman Filtering algorithm is used to restrain the heading divergence at the middle moment of two steps. The walking experimental results indicate that the MTS-ZUPT algorithm can effectively restrain the heading error diffusion without the assistance of geomagnetic heading. When the MTS-ZUPT method was integrated with GA method, the smartphone navigation system can autonomously judge the using-mode and compensate the heading errors. The pedestrian positioning accuracy is significantly improved and the walking error is only 1.4% to 2.0% of the walking distance in using-mode experiments of the smartphone.

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user's motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application

Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle’s backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation.

Autonomous Landmark Calibration Method for Indoor Localization

Machine-generated data expansion is a global phenomenon in recent Internet services. The proliferation of mobile communication and smart devices has increased the utilization of machine-generated data significantly. One of the most promising applications of machine-generated data is the estimation of the location of smart devices. The motion sensors integrated into smart devices generate continuous data that can be used to estimate the location of pedestrians in an indoor environment. We focus on the estimation of the accurate location of smart devices by determining the landmarks appropriately for location error calibration. In the motion sensor-based location estimation, the proposed threshold control method determines valid landmarks in real time to avoid the accumulation of errors. A statistical method analyzes the acquired motion sensor data and proposes a valid landmark for every movement of the smart devices. Motion sensor data used in the testbed are collected from the actual measurements taken throughout a commercial building to demonstrate the practical usefulness of the proposed method.

An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study

Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter.

Improving the accuracy of estimates of animal path and travel distance using GPS drift-corrected dead reckoning

Route taken and distance travelled are important parameters for studies of animal locomotion. They are often measured using a collar equipped with GPS. Collar weight restrictions limit battery size, which leads to a compromise between collar operating life and GPS fix rate. In studies that rely on linear interpolation between intermittent GPS fixes, path tortuosity will often lead to inaccurate path and distance travelled estimates. Here, we investigate whether GPS-corrected dead reckoning can improve the accuracy of localization and distance travelled estimates while maximizing collar operating life. Custom-built tracking collars were deployed on nine freely exercising domestic dogs to collect high fix rate GPS data. Simulations were carried out to measure the extent to which combining accelerometer-based speed and magnetometer heading estimates (dead reckoning) with low fix rate GPS drift correction could improve the accuracy of path and distance travelled estimates. In our study, median 2-dimensional root-mean-squared (2D-RMS) position error was between 158 and 463 m (median path length 16.43 km) and distance travelled was underestimated by between 30% and 64% when a GPS position fix was taken every 5 min. Dead reckoning with GPS drift correction (1 GPS fix every 5 min) reduced 2D-RMS position error to between 15 and 38 m and distance travelled to between an underestimation of 2% and an overestimation of 5%. Achieving this accuracy from GPS alone would require approximately 12 fixes every minute and result in a battery life of approximately 11 days; dead reckoning reduces the number of fixes required, enabling a collar life of approximately 10 months. Our results are generally applicable to GPS-based tracking studies of quadrupedal animals and could be applied to studies of energetics, behavioral ecology, and locomotion. This low-cost approach overcomes the limitation of low fix rate GPS and enables the long-term deployment of lightweight GPS collars.

Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning

BACKGROUND

Research on wild animal ecology is increasingly employing GPS telemetry in order to determine animal movement. However, GPS systems record position intermittently, providing no information on latent position or track tortuosity. High frequency GPS have high power requirements, which necessitates large batteries (often effectively precluding their use on small animals) or reduced deployment duration. Dead-reckoning is an alternative approach which has the potential to 'fill in the gaps' between less resolute forms of telemetry without incurring the power costs. However, although this method has been used in aquatic environments, no explicit demonstration of terrestrial dead-reckoning has been presented.

RESULTS

We perform a simple validation experiment to assess the rate of error accumulation in terrestrial dead-reckoning. In addition, examples of successful implementation of dead-reckoning are given using data from the domestic dog Canus lupus, horse Equus ferus, cow Bos taurus and wild badger Meles meles.

CONCLUSIONS

This study documents how terrestrial dead-reckoning can be undertaken, describing derivation of heading from tri-axial accelerometer and tri-axial magnetometer data, correction for hard and soft iron distortions on the magnetometer output, and presenting a novel correction procedure to marry dead-reckoned paths to ground-truthed positions. This study is the first explicit demonstration of terrestrial dead-reckoning, which provides a workable method of deriving the paths of animals on a step-by-step scale. The wider implications of this method for the understanding of animal movement ecology are discussed.

Afocal optical flow sensor for reducing vertical height sensitivity in indoor robot localization and navigation

This paper introduces a novel afocal optical flow sensor (OFS) system for odometry estimation in indoor robotic navigation. The OFS used in computer optical mouse has been adopted for mobile robots because it is not affected by wheel slippage. Vertical height variance is thought to be a dominant factor in systematic error when estimating moving distances in mobile robots driving on uneven surfaces. We propose an approach to mitigate this error by using an afocal (infinite effective focal length) system. We conducted experiments in a linear guide on carpet and three other materials with varying sensor heights from 30 to 50 mm and a moving distance of 80 cm. The same experiments were repeated 10 times. For the proposed afocal OFS module, a 1 mm change in sensor height induces a 0.1% systematic error; for comparison, the error for a conventional fixed-focal-length OFS module is 14.7%. Finally, the proposed afocal OFS module was installed on a mobile robot and tested 10 times on a carpet for distances of 1 m. The average distance estimation error and standard deviation are 0.02% and 17.6%, respectively, whereas those for a conventional OFS module are 4.09% and 25.7%, respectively.

Differential neural network configuration during human path integration

Path integration is a fundamental skill for navigation in both humans and animals. Despite recent advances in unraveling the neural basis of path integration in animal models, relatively little is known about how path integration operates at a neural level in humans. Previous attempts to characterize the neural mechanisms used by humans to visually path integrate have suggested a central role of the hippocampus in allowing accurate performance, broadly resembling results from animal data. However, in recent years both the central role of the hippocampus and the perspective that animals and humans share similar neural mechanisms for path integration has come into question. The present study uses a data driven analysis to investigate the neural systems engaged during visual path integration in humans, allowing for an unbiased estimate of neural activity across the entire brain. Our results suggest that humans employ common task control, attention and spatial working memory systems across a frontoparietal network during path integration. However, individuals differed in how these systems are configured into functional networks. High performing individuals were found to more broadly express spatial working memory systems in prefrontal cortex, while low performing individuals engaged an allocentric memory system based primarily in the medial occipito-temporal region. These findings suggest that visual path integration in humans over short distances can operate through a spatial working memory system engaging primarily the prefrontal cortex and that the differential configuration of memory systems recruited by task control networks may help explain individual biases in spatial learning strategies.

Task partitioning in a robot swarm: object retrieval as a sequence of subtasks with direct object transfer

We study task partitioning in the context of swarm robotics. Task partitioning is the decomposition of a task into subtasks that can be tackled by different workers. We focus on the case in which a task is partitioned into a sequence of subtasks that must be executed in a certain order. This implies that the subtasks must interface with each other, and that the output of a subtask is used as input for the subtask that follows. A distinction can be made between task partitioning with direct transfer and with indirect transfer. We focus our study on the first case: The output of a subtask is directly transferred from an individual working on that subtask to an individual working on the subtask that follows. As a test bed for our study, we use a swarm of robots performing foraging. The robots have to harvest objects from a source, situated in an unknown location, and transport them to a home location. When a robot finds the source, it memorizes its position and uses dead reckoning to return there. Dead reckoning is appealing in robotics, since it is a cheap localization method and it does not require any additional external infrastructure. However, dead reckoning leads to errors that grow in time if not corrected periodically. We compare a foraging strategy that does not make use of task partitioning with one that does. We show that cooperation through task partitioning can be used to limit the effect of dead reckoning errors. This results in improved capability of locating the object source and in increased performance of the swarm. We use the implemented system as a test bed to study benefits and costs of task partitioning with direct transfer. We implement the system with real robots, demonstrating the feasibility of our approach in a foraging scenario.

Step length estimation using handheld inertial sensors

In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user's step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor's signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand's motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.

Enhancing positioning accuracy in urban terrain by fusing data from a GPS receiver, inertial sensors, stereo-camera and digital maps for pedestrian navigation

The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian's steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences etc. This limits position inaccuracy to ca. 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS.

Odometry and laser scanner fusion based on a discrete extended Kalman Filter for robotic platooning guidance

This paper describes a relative localization system used to achieve the navigation of a convoy of robotic units in indoor environments. This positioning system is carried out fusing two sensorial sources: (a) an odometric system and (b) a laser scanner together with artificial landmarks located on top of the units. The laser source allows one to compensate the cumulative error inherent to dead-reckoning; whereas the odometry source provides less pose uncertainty in short trajectories. A discrete Extended Kalman Filter, customized for this application, is used in order to accomplish this aim under real time constraints. Different experimental results with a convoy of Pioneer P3-DX units tracking non-linear trajectories are shown. The paper shows that a simple setup based on low cost laser range systems and robot built-in odometry sensors is able to give a high degree of robustness and accuracy to the relative localization problem of convoy units for indoor applications.

Vestibular information is required for dead reckoning in the rat

Dead reckoning is an on-line form of spatial navigation used by an animal to identify its present location and return directly to a starting location, even after circuitous outward trips. At present, it is not known which of several self-movement cues (efferent copy from movement commands, proprioceptive information, sensory flow, or vestibular information) are used to compute homeward trajectories. To determine whether vestibular information is important for dead reckoning, the impact of chemical labyrinthectomy was evaluated in a test that demanded on-line computation of a homeward trajectory. Rats were habituated to leave a refuge that was visible from all locations on a circular table to forage for large food pellets, which they carried back to the refuge to eat. Two different probe trials were given: (1) the rats foraged from the same spatial location from a hidden refuge in the light and so were able to use visual cues to navigate; (2) the same procedure took place in the dark, constraining the animals to dead reckon. Although control rats carried food directly and rapidly back to the refuge on both probes, the rats with vestibular lesions were able to do so on the hidden refuge but not on the dark probe. The scores of vestibular reflex tests predicted the dead reckoning deficit. The vestibular animals were also impaired in learning a new piloting task. This is the first unambiguous demonstration that vestibular information is used in dead reckoning and also contributes to piloting.

Path integration absent in scent-tracking fimbria-fornix rats: evidence for hippocampal involvement in "sense of direction" and "sense of distance" using self-movement cues

Allothetic and idiothetic navigation strategies use very different cue constellations and computational processes. Allothetic navigation requires the use of the relationships between relatively stable external (visual, olfactory, auditory) cues, whereas idiothetic navigation requires the integration of cues generated by self-movement and/or efferent copy of movement commands. The flexibility with which animals can switch between these strategies and the neural structures that support these strategies are not well understood. By capitalizing on the proclivity of foraging rats to carry large food pellets back to a refuge for eating, the present study examined the contribution of the hippocampus to the use of allothetic versus idiothetic navigation strategies. Control rats and fimbria-fornix-ablated rats were trained to follow linear, polygonal, and octagonal scent trails that led to a piece of food. The ability of the rats to return to the refuge with the food via the shortest route using allothetic cues (visual cues and/or the odor trail available) or using ideothetic cues (the odor trail removed and the rats blindfolded or tested in infrared light) was examined. Control rats "closed the polygon" by returning directly home in all cue conditions. Fimbria-fornix rats successfully used allothetic cues (closed the polygon using visual cues or tracked back on the string) but were insensitive to the direction and distance of the refuge and were lost when restricted to idiothetic cues. The results support the hypothesis that the hippocampal formation is necessary for navigation requiring the integration of idiothetic cues.

Path integration and cognitive mapping in a continuous attractor neural network model

A minimal synaptic architecture is proposed for how the brain might perform path integration by computing the next internal representation of self-location from the current representation and from the perceived velocity of motion. In the model, a place-cell assembly called a "chart" contains a two-dimensional attractor set called an "attractor map" that can be used to represent coordinates in any arbitrary environment, once associative binding has occurred between chart locations and sensory inputs. In hippocampus, there are different spatial relations among place fields in different environments and behavioral contexts. Thus, the same units may participate in many charts, and it is shown that the number of uncorrelated charts that can be encoded in the same recurrent network is potentially quite large. According to this theory, the firing of a given place cell is primarily a cooperative effect of the activity of its neighbors on the currently active chart. Therefore, it is not particularly useful to think of place cells as encoding any particular external object or event. Because of its recurrent connections, hippocampal field CA3 is proposed as a possible location for this "multichart" architecture; however, other implementations in anatomy would not invalidate the main concepts. The model is implemented numerically both as a network of integrate-and-fire units and as a "macroscopic" (with respect to the space of states) description of the system, based on a continuous approximation defined by a system of stochastic differential equations. It provides an explanation for a number of hitherto perplexing observations on hippocampal place fields, including doubling, vanishing, reshaping in distorted environments, acquiring directionality in a two-goal shuttling task, rapid formation in a novel environment, and slow rotation after disorientation. The model makes several new predictions about the expected properties of hippocampal place cells and other cells of the proposed network.