The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies

Indoor Mapping with Entertainment Devices: Evaluating the Impact of Different Mapping Strategies for Microsoft HoloLens 2 and Apple iPhone 14 Pro

Due to their low cost and portability, using entertainment devices for indoor mapping applications has become a hot research topic. However, the impact of user behavior on indoor mapping evaluation with entertainment devices is often overlooked in previous studies. This article aims to assess the indoor mapping performance of entertainment devices under different mapping strategies. We chose two entertainment devices, the HoloLens 2 and iPhone 14 Pro, for our evaluation work. Based on our previous mapping experience and user habits, we defined four simplified indoor mapping strategies: straight-forward mapping (SFM), left-right alternating mapping (LRAM), round-trip straight-forward mapping (RT-SFM), and round-trip left-right alternating mapping (RT-LRAM). First, we acquired triangle mesh data under each strategy with the HoloLens 2 and iPhone 14 Pro. Then, we compared the changes in data completeness and accuracy between the different devices and indoor mapping applications. Our findings show that compared to the iPhone 14 Pro, the triangle mesh accuracy acquired by the HoloLens 2 has more stable performance under different strategies. Notably, the triangle mesh data acquired by the HoloLens 2 under the RT-LRAM strategy can effectively compensate for missing wall and floor surfaces, mainly caused by furniture occlusion and the low frame rate of the depth-sensing camera. However, the iPhone 14 Pro is more efficient in terms of mapping completeness and can acquire a complete triangle mesh more quickly than the HoloLens 2. In summary, choosing an entertainment device for indoor mapping requires a combination of specific needs and scenes. If accuracy and stability are important, the HoloLens 2 is more suitable; if efficiency and completeness are important, the iPhone 14 Pro is better.

Finding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer

One of the most challenging topics in robotics is simultaneous localization and mapping (SLAM) in the indoor environments. Due to the fact that Global Navigation Satellite Systems cannot be successfully used in such environments, different data sources are used for this purpose, among others light detection and ranging (LiDARs ), which have advanced from numerous other technologies. Other embedded sensors can be used along with LiDARs to improve SLAM accuracy, e.g. the ones available in the Inertial Measurement Units and wheel odometry sensors. Evaluation of different SLAM algorithms and possible hardware configurations in real environments is time consuming and expensive. In our study, we evaluate the accuracy of mapping and localization (based on Absolute Trajectory Error and Relative Pose Error). Our use case is a robot used for room decontamination. The results for a small room show that for our robot the best hardware configuration consists of three LiDARs 2D, IMU and wheel odometry sensors. On the other hand, for long hallways, a configuration with one LiDAR 3D sensor and IMU works better and more stable. We also described a general approach together with tools and procedures that can be used to find the best sensor setup in simulation.

GNSS-RTK Adaptively Integrated with LiDAR/IMU Odometry for Continuously Global Positioning in Urban Canyons

Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used.

Point Cloud Validation: On the Impact of Laser Scanning Technologies on the Semantic Segmentation for BIM Modeling and Evaluation

Building Information models created from laser scanning inputs are becoming increasingly commonplace, but the automation of the modeling and evaluation is still a subject of ongoing research. Current advancements mainly target the data interpretation steps, i.e., the instance and semantic segmentation by developing advanced deep learning models. However, these steps are highly influenced by the characteristics of the laser scanning technologies themselves, which also impact the reconstruction/evaluation potential. In this work, the impact of different data acquisition techniques and technologies on these procedures is studied. More specifically, we quantify the capacity of static, trolley, backpack, and head-worn mapping solutions and their semantic segmentation results such as for BIM modeling and analyses procedures. For the analysis, international standards and specifications are used wherever possible. From the experiments, the suitability of each platform is established, along with the pros and cons of each system. Overall, this work provides a much needed update on point cloud validation that is needed to further fuel BIM automation.

Visual Collaboration Leader-Follower UAV-Formation for Indoor Exploration

UAVs operating in a leader-follower formation demand the knowledge of the relative pose between the collaborating members. This necessitates the RF-communication of this information which increases the communication latency and can easily result in lost data packets. In this work, rather than relying on this autopilot data exchange, a visual scheme using passive markers is presented. Each formation-member carries passive markers in a RhOct configuration. These markers are visually detected and the relative pose of the members is on-board determined, thus eliminating the need for RF-communication. A reference path is then evaluated for each follower that tracks the leader and maintains a constant distance between the formation-members. Experimental studies show a mean position detection error (5 × 5 × 10cm) or less than 0.0031% of the available workspace [0.5 up to 5m, 50.43° × 38.75° Field of View (FoV)]. The efficiency of the suggested scheme against varying delays are examined in these studies, where it is shown that a delay up to 1.25s can be tolerated for the follower to track the leader as long as the latter one remains within its FoV.

The Combined Use of SLAM Laser Scanning and TLS for the 3D Indoor Mapping

An efficient 3D survey of a complex indoor environment remains a challenging task, especially if the accuracy requirements for the geometric data are high for instance in building information modeling (BIM) or construction. The registration of non-overlapping terrestrial laser scanning (TLS) point clouds is laborious. We propose a novel indoor mapping strategy that uses a simultaneous localization and mapping (SLAM) laser scanner (LS) to support the building-scale registration of non-overlapping TLS point clouds in order to reconstruct comprehensive building floor/3D maps. This strategy improves efficiency since it allows georeferenced TLS data to only be collected from those parts of the building that require such accuracy. The rest of the building is measured with SLAM LS accuracy. Based on the results of the case study, the introduced method can locate non-overlapping TLS point clouds with an accuracy of 18–51 mm using target sphere comparison.

Real-Time Locating System in Production Management

Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can enrich the available information, these systems started to gain attention in industrial environments in recent years. This paper provides a review of the possible technologies and applications related to production control and logistics, quality management, safety, and efficiency monitoring. This work also provides a workflow to clarify the steps of a typical real-time locating system project, including the cleaning, pre-processing, and analysis of the data to provide a guideline and reference for research and development of indoor positioning-based manufacturing solutions.

Autonomous Indoor Scanning System Collecting Spatial and Environmental Data for Efficient Indoor Monitoring and Control

As various activities related to entertainment, business, shopping, and conventions are done increasingly indoors, the demand for indoor spatial information and indoor environmental data is growing. Unlike the case of outdoor environments, obtaining spatial information in indoor environments is difficult. Given the absence of GNSS (Global Navigation Satellite System) signals, various technologies for indoor positioning, mapping and modeling have been proposed. Related business models for indoor space services, safety, convenience, facility management, and disaster response, moreover, have been suggested. An autonomous scanning system for collection of indoor spatial and environmental data is proposed in this paper. The proposed system can be utilized to collect spatial dimensions suitable for extraction of a two-dimensional indoor drawing and obtainment of spatial imaging as well as indoor environmental data on temperature, humidity and particulate matter. For these operations, the system has two modes, manual and autonomous. The main function of the systems is autonomous mode, and the manual mode is implemented additionally. It can be applied in facilities without infrastructure for indoor data collection, such as for routine indoor data collection purposes, and it can also be used for immediate indoor data collection in cases of emergency (e.g., accidents, disasters).

A Low-Cost Method of Improving the GNSS/SINS Integrated Navigation System Using Multiple Receivers

GNSS (global navigation satellite system) and SINS (strap-down inertial navigation system) integrated navigation systems have been the apparatus for providing reliable and stable position and velocity information (PV). Commonly, there are two solutions to improve the GNSS/SINS integration navigation system accuracy, i.e., employing GNSS with higher position accuracy in the integration system or utilizing the high-grade inertial measurement unit (IMU) to construct the integration system. However, technologies such as RTK (real-time kinematic) and PPP (precise point positioning) that improve GNSS positioning accuracy have higher costs and they cannot work under high dynamic environments. Also, an IMU with high accuracy will lead to a higher cost and larger volume, therefore, a low-cost method to enhance the GNSS/SINS integration accuracy is of great significance. In this paper, multiple receivers based on the GNSS/SINS integrated navigation system are proposed with the aim of providing more precise PV information. Since the chip-scale receivers are cheap, the deployment of multiple receivers in the GNSS/SINS integration will not significantly increase the cost. In addition, two different filtering methods with central and cascaded structure are employed to process the multiple receivers and SINS integration. In the centralized integration filter method, measurements from multiple receivers are directly processed to estimate the SINS errors state vectors. However, the computation load increases heavily due to the rising dimension of the measurement vector. Therefore, a cascaded integration filter structure is also employed to distribute the processing of the multiple receiver and SINS integration. In the cascaded processing method, each receiver is regarded as an individual “sensor”, and a standard federated Kalman filter (FKF) is implemented to obtain an optimal estimation of the navigation solutions. In this paper, a simulation and a field tests are carried out to assess the influence of the number of receivers on the PV accuracy. A detailed analysis of these position and velocity results is presented and the improvements in the PV accuracy demonstrate the effectiveness of the proposed method.

MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis

Indoor mobile mapping techniques are important for indoor navigation and indoor modeling. As an efficient method, Simultaneous Localization and Mapping (SLAM) based on Light Detection and Ranging (LiDAR) has been applied for fast indoor mobile mapping. It can quickly construct high-precision indoor maps in a certain small region. However, with the expansion of the mapping area, SLAM-based mapping methods face many difficulties, such as loop closure detection, large amounts of calculation, large memory occupation, and limited mapping precision. In this paper, we propose a distributed indoor mapping scheme based on control-network-aided SLAM to solve the problem of mapping for large-scale environments. Its effectiveness is analyzed from the relative accuracy and absolute accuracy of the mapping results. The experimental results show that the relative accuracy can reach 0.08 m, an improvement of 49.8% compared to the mapping result without loop closure. The absolute accuracy can reach 0.13 m, which proves the method’s feasibility for distributed mapping. The accuracies under different numbers of control points are also compared to find the suitable structure of the control network.

Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications

The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments.

Feasibility of Discriminating UAV Propellers Noise from Distress Signals to Locate People in Enclosed Environments Using MEMS Microphone Arrays

Detecting and finding people are complex tasks when visibility is reduced. This happens, for example, if a fire occurs. In these situations, heat sources and large amounts of smoke are generated. Under these circumstances, locating survivors using thermal or conventional cameras is not possible and it is necessary to use alternative techniques. The challenge of this work was to analyze if it is feasible the integration of an acoustic camera, developed at the University of Valladolid, on an unmanned aerial vehicle (UAV) to locate, by sound, people who are calling for help, in enclosed environments with reduced visibility. The acoustic array, based on MEMS (micro-electro-mechanical system) microphones, locates acoustic sources in space, and the UAV navigates autonomously by closed enclosures. This paper presents the first experimental results locating the angles of arrival of multiple sound sources, including the cries for help of a person, in an enclosed environment. The results are promising, as the system proves able to discriminate the noise generated by the propellers of the UAV, at the same time it identifies the angles of arrival of the direct sound signal and its first echoes reflected on the reflective surfaces.

A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks.

A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)

Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Hebei, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.