On measuring the accuracy of SLAM algorithms

The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem

We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics.

On the precision of 6 DoF IMU-LiDAR based localization in GNSS-denied scenarios

Positioning and navigation represent relevant topics in the field of robotics, due to their multiple applications in real-world scenarios, ranging from autonomous driving to harsh environment exploration. Despite localization in outdoor environments is generally achieved using a Global Navigation Satellite System (GNSS) receiver, global navigation satellite system-denied environments are typical of many situations, especially in indoor settings. Autonomous robots are commonly equipped with multiple sensors, including laser rangefinders, IMUs, and odometers, which can be used for mapping and localization, overcoming the need for global navigation satellite system data. In literature, almost no information can be found on the positioning accuracy and precision of 6 Degrees of Freedom Light Detection and Ranging (LiDAR) localization systems, especially for real-world scenarios. In this paper, we present a short review of state-of-the-art light detection and ranging localization methods in global navigation satellite system-denied environments, highlighting their advantages and disadvantages. Then, we evaluate two state-of-the-art Simultaneous Localization and Mapping (SLAM) systems able to also perform localization, one of which implemented by us. We benchmark these two algorithms on manually collected dataset, with the goal of providing an insight into their attainable precision in real-world scenarios. In particular, we present two experimental campaigns, one indoor and one outdoor, to measure the precision of these algorithms. After creating a map for each of the two environments, using the simultaneous localization and mapping part of the systems, we compute a custom localization error for multiple, different trajectories. Results show that the two algorithms are comparable in terms of precision, having a similar mean translation and rotation errors of about 0.01 m and 0.6°, respectively. Nevertheless, the system implemented by us has the advantage of being modular, customizable and able to achieve real-time performance.

Analysis of Lidar Actuator System Influence on the Quality of Dense 3D Point Cloud Obtained with SLAM

Mobile mapping technologies, based on techniques such as simultaneous localization and mapping (SLAM) and surface-from-motion (SfM), are being vigorously developed both in the scientific community and in industry. They are crucial concepts for automated 3D surveying and autonomous vehicles. For various applications, rotating multiline scanners, manufactured, for example, by Velodyne and Ouster, are utilized as the main sensor of the mapping hardware system. However, their principle of operation has a substantial drawback, as their scanning pattern creates natural gaps between the scanning lines. In some models, the vertical lidar field of view can also be severely limited. To overcome these issues, more sensors could be employed, which would significantly increase the cost of the mapping system. Instead, some investigators have added a tilting or rotating motor to the lidar. Although the effectiveness of such a solution is usually clearly visible, its impact on the quality of the acquired 3D data has not yet been investigated. This paper presents an adjustable mapping system, which allows for switching between a stable, tilting or fully rotating lidar position. A simple experiment in a building corridor was performed, simulating the conditions of a mobile robot passing through a narrow tunnel: a common setting for applications, such as mining surveying or industrial facility inspection. A SLAM algorithm is utilized to create a coherent 3D point cloud of the mapped corridor for three settings of the sensor movement. The extent of improvement in the 3D data quality when using the tilting and rotating lidar, compared to keeping a stable position, is quantified. Different metrics are proposed to account for different aspects of the 3D data quality, such as completeness, density and geometry coherence. The ability of SLAM algorithms to faithfully represent selected objects appearing in the mapped scene is also examined. The results show that the fully rotating solution is optimal in terms of most of the metrics analyzed. However, the improvement observed from a horizontally mounted sensor to a tilting sensor was the most significant.

Meaningful Test and Evaluation of Indoor Localization Systems in Semi-Controlled Environments

Despite their enormous potential, the use of indoor localization systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders' trust in the systems' performance as a consequence of insufficient use of test and evaluation (T&E) methodologies. The heterogeneous nature of ILS, their influences, and their applications pose various challenges for the design of a methodology that provides meaningful results. Methodologies for building-wide testing exist, but their use is mostly limited to associated indoor localization competitions. In this work, the T&E 4iLoc Framework is proposed-a methodology for T&E of indoor localization systems in semi-controlled environments based on a system-level and black-box approach. In contrast to building-wide testing, T&E in semi-controlled environments, such as test halls, is characterized by lower costs, higher reproducibility, and better comparability of the results. The limitation of low transferability to real-world applications is addressed by an application-driven design approach. The empirical validation of the T&E 4iLoc Framework, based on the examination of a contour-based light detection and ranging (LiDAR) ILS, an ultra wideband ILS, and a camera-based ILS for the application of automated guided vehicles in warehouse operation, demonstrates the benefits of T&E with the T&E 4iLoc Framework.

Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework

Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications.

LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

With the emerging interest in the autonomous driving level at 4 and 5 comes a necessity to provide accurate and versatile frameworks to evaluate the algorithms used in autonomous vehicles. There is a clear gap in the field of autonomous driving simulators. It covers testing and parameter tuning of a key component of autonomous driving systems, SLAM, frameworks targeting off-road and safety-critical environments. It also includes taking into consideration the non-idealistic nature of the real-life sensors, associated phenomena and measurement errors. We created a LiDAR simulator that delivers accurate 3D point clouds in real time. The point clouds are generated based on the sensor placement and the LiDAR type that can be set using configurable parameters. We evaluate our solution based on comparison of the results using an actual device, Velodyne VLP-16, on real-life tracks and the corresponding simulations. We measure the error values obtained using Google Cartographer SLAM algorithm and the distance between the simulated and real point clouds to verify their accuracy. The results show that our simulation (which incorporates measurement errors and the rolling shutter effect) produces data that can successfully imitate the real-life point clouds. Due to dedicated mechanisms, it is compatible with the Robotic Operating System (ROS) and can be used interchangeably with data from actual sensors, which enables easy testing, SLAM algorithm parameter tuning and deployment.

Composite clustering normal distribution transform algorithm

Scan registration is a fundamental step for the simultaneous localization and mapping of mobile robot. The accuracy of scan registration is critical for the quality of mapping and the accuracy of robot navigation. During all of the scan registration methods, normal distribution transform is an efficient and wild-using one. But normal distribution transform will lead to the unreasonable interruption when splitting the grid and can’t express the points’ local geometric feature by prefixed grid. In this article, we propose a novel method, composite clustering normal distribution transform, which comprises the density-based clustering and k-means clustering to aggregate the points with similar local distributing feature. It takes singular value decomposition to judge the suitable degree of one cluster for further division. Meanwhile, to avoid the radiating phenomenon of LIDAR in measuring the points’ distance, we propose a method based on trigonometric to measure the internal distance. The clustering method in composite clustering normal distribution transform could ensure the expression of LIDAR’s local distribution and matching accuracy. The experimental result demonstrates that our method is more accurate and more stable than the normal distribution transform and iterative closest point methods.

Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.

Common Data Fusion Framework: An open-source Common Data Fusion Framework for space robotics

Multisensor data fusion plays a vital role in providing autonomous systems with environmental information crucial for reliable functioning. In this article, we summarize the modular structure of the newly developed and released Common Data Fusion Framework and explain how it is used. Sensor data are registered and fused within the Common Data Fusion Framework to produce comprehensive 3D environment representations and pose estimations. The proposed software components to model this process in a reusable manner are presented through a complete overview of the framework, then the provided data fusion algorithms are listed, and through the case of 3D reconstruction from 2D images, the Common Data Fusion Framework approach is exemplified. The Common Data Fusion Framework has been deployed and tested in various scenarios that include robots performing operations of planetary rover exploration and tracking of orbiting satellites.

Cartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler

This paper presents the use of Google’s simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help of scheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scans while maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose’s generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy.

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure–from–motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below.

IMU-Assisted 2D SLAM Method for Low-Texture and Dynamic Environments

Generally, the key issues of 2D LiDAR-based simultaneous localization and mapping (SLAM) for indoor application include data association (DA) and closed-loop detection. Particularly, a low-texture environment, which refers to no obvious changes between two consecutive scanning outputs, with moving objects existing in the environment will bring great challenges on DA and the closed-loop detection, and the accuracy and consistency of SLAM may be badly affected. There is not much literature that addresses this issue. In this paper, a mapping strategy is firstly exploited to improve the performance of the 2D SLAM in dynamic environments. Secondly, a fusion method which combines the IMU sensor with a 2D LiDAR, based on framework of extended Kalman Filter (EKF), is proposed to enhance the performance under low-texture environments. In the front-end of the proposed SLAM method, initial motion estimation is obtained from the output of EKF, and it can be taken as the initial pose for the scan matching problem. Then the scan matching problem can be optimized by the Levenberg–Marquardt (LM) algorithm. For the back-end optimization, a sparse pose adjustment (SPA) method is employed. To improve the accuracy, the grid map is updated with the bicubic interpolation method for derivative computing. With the improvements both in the DA process and the back-end optimization stage, the accuracy and consistency of SLAM results in low-texture environments is enhanced. Qualitative and quantitative experiments with open-loop and closed-loop cases have been conducted and the results are analyzed, confirming that the proposed method is effective in low-texture and dynamic indoor environments.

A fully autonomous terrestrial bat-like acoustic robot

Echolocating bats rely on active sound emission (echolocation) for mapping novel environments and navigating through them. Many theoretical frameworks have been suggested to explain how they do so, but few attempts have been made to build an actual robot that mimics their abilities. Here, we present the ‘Robat’—a fully autonomous bat-like terrestrial robot that relies on echolocation to move through a novel environment while mapping it solely based on sound. Using the echoes reflected from the environment, the Robat delineates the borders of objects it encounters, and classifies them using an artificial neural-network, thus creating a rich map of its environment. Unlike most previous attempts to apply sonar in robotics, we focus on a biological bat-like approach, which relies on a single emitter and two ears, and we apply a biological plausible signal processing approach to extract information about objects’ position and identity.

Many animals are able of mapping a new environment even while moving through it for the first time. Bats can do this by emitting sound and extracting information from the echoes reflected from objects in their surroundings. In this study, we mimicked this ability by developing a robot that emits sound like a bat and analyzes the returning echoes to generate a map of space. Our Robat had an ultrasonic speaker mimicking the bat’s mouth and two ultrasonic microphones mimicking its ears. It moved autonomously through novel out-doors environments and mapped them using sound only. It was able to negotiate obstacles and move around them, to avoid dead-ends and even to recognize if the object in front of it is a plant or not. We show the great potential of using sound for future robotic applications.

A Novel Real-Time Reference Key Frame Scan Matching Method

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions' environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.

SLAM++-A highly efficient and temporally scalable incremental SLAM framework

The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the maximum likelihood estimation converts to a nonlinear least squares problem. Efficient solutions to nonlinear least squares exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localization and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding.

This paper introduces a general framework for incremental maximum likelihood estimation called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping, which uses information theory measures to integrate only informative and non-redundant contributions to the state representation. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations used in nonlinear least squares. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general.

High Accuracy Monocular SFM and Scale Correction for Autonomous Driving

We present a real-time monocular visual odometry system that achieves high accuracy in real-world autonomous driving applications. First, we demonstrate robust monocular SFM that exploits multithreading to handle driving scenes with large motions and rapidly changing imagery. To correct for scale drift, we use known height of the camera from the ground plane. Our second contribution is a novel data-driven mechanism for cue combination that allows highly accurate ground plane estimation by adapting observation covariances of multiple cues, such as sparse feature matching and dense inter-frame stereo, based on their relative confidences inferred from visual data on a per-frame basis. Finally, we demonstrate extensive benchmark performance and comparisons on the challenging KITTI dataset, achieving accuracy comparable to stereo and exceeding prior monocular systems. Our SFM system is optimized to output pose within 50 ms in the worst case, while average case operation is over 30 fps. Our framework also significantly boosts the accuracy of applications like object localization that rely on the ground plane.

A fast and accurate approximation for planar pose graph optimization

This work investigates the pose graph optimization problem, which arises in maximum likelihood approaches to simultaneous localization and mapping (SLAM). State-of-the-art approaches have been demonstrated to be very efficient in medium- and large-sized scenarios; however, their convergence to the maximum likelihood estimate heavily relies on the quality of the initial guess. We show that, in planar scenarios, pose graph optimization has a very peculiar structure. The problem of estimating robot orientations from relative orientation measurements is a quadratic optimization problem (after computing suitable regularization terms); moreover, given robot orientations, the overall optimization problem becomes quadratic. We exploit these observations to design an approximation of the maximum likelihood estimate, which does not require the availability of an initial guess. The approximation, named LAGO (Linear Approximation for pose Graph Optimization), can be used as a stand-alone tool or can bootstrap state-of-the-art techniques, reducing the risk of being trapped in local minima. We provide analytical results on existence and sub-optimality of LAGO, and we discuss the factors influencing its quality. Experimental results demonstrate that LAGO is accurate in common SLAM problems. Moreover, it is remarkably faster than state-of-the-art techniques, and is able to solve very large-scale problems in a few seconds.

Simultaneous localization and mapping with multimodal probability distributions

Simultaneous Localization and Mapping (SLAM) has focused on noisy but unique data associations resulting in linear Gaussian uncertainty models. However, a unique decision is often not possible using only local information, giving rise to ambiguities that have to be resolved globally during optimization. To solve this problem, the pose graph data structure is extended here by multimodal constraints modeled by mixtures of Gaussians (MoG). Furthermore, optimization methods for this novel formulation are introduced, namely (a) robust iteratively reweighted least squares, and (b) Prefilter Stochastic Gradient Descent (SGD) where a preprocessing step determines globally consistent modes before applying SGD. In addition, a variant of the Prefilter method (b) is introduced in form of (c) Prefilter Levenberg–Marquardt. The methods are compared with traditional state-of-the-art optimization methods including (d) Stochastic Gradient Descent and (e) Levenberg–Marquardt as well as (f) Particle filter SLAM and with (g) an optimal exhaustive algorithm. Experiments show that ambiguities significantly impact state-of-the-art methods, and that the novel Prefilter methods (b) and (c) perform best. This is further substantiated with experiments using real-world data. To this end, a method to generate MoG constraints from a plane-based registration algorithm is introduced and used for 3D SLAM under ambiguities.