Exactly sparse delayed state filter on Lie groups for long-term pose graph SLAM

Simultaneous localization and mapping (SLAM) is a core element of every autonomous mobile robot. The underlying engine of a SLAM system is its back-end, which aims at optimally estimating the trajectory and map of the environment based on sensor data abstractions. Over the past decade, SLAM solutions based on graph optimization approaches prevailed over the filtering based solutions, since they dominated in performance over a wider range of applications. In this paper we propose a novel filtering based SLAM back-end based on the exactly sparse delayed state filter (ESDSF) derived on Lie groups (LG-ESDSF). The proposed filter retains all the good characteristics of the classic ESDSF, but also respects the state space geometry by employing filtering equations directly on Lie groups. We have compared our SLAM system with two current state-of-the-art SLAM solutions, namely ORB-SLAM and LSD-SLAM, on the KITTI vision benchmark suite. Test results show that the proposed SLAM based on the LG-ESDSF back-end can achieve same level of accuracy as the methods based on the graph optimization techniques, while maintaining lower computation times.

Graphs and matrices are widely used to represent maps in solutions for Simultaneous Localization and Mapping (SLAM). Space representation is crucial for map creation, finding the shortest path, planning a trajectory or route, tracking landmarks, etc. The choice of the data structure can present disadvantages such as more memory consumption when using matrices, or, the need of a transforming function to represent a map when graphs (as structure) are applied as structure. This paper propose a representing map using a storing structure that allows dynamic growth and optimization of memory consumption in SLAM. The so-called MPTE-SLAM structure, proposed here, suggests the usage of a sparse matrix of occupancy matrices to represent a map for SLAM. The goal of this approach is to reduce the use of memory (if compared with traditional metric maps also) also having a compact representation suitable for path planning and other tasks for autonomous robots; without the need of a transforming function (if compared with graph based solutions that return a map). This approach allows the addition and updating of values of the sub-matrices quickly and with dynamic growth on the overall map within the structure in the same way graphs permit; also it is suitable for a real-time implementation of solutions for SLAM. For implementation and testing, a non-holonomic mobile robot in an indoor environment was used. The final results showed that the MPTE-SLAM structure uses less memory than metric maps when optimal sub-matrices sizes are applied. Through experimentation an optimal size for submatrices (10x10) was determined; this size yielded a memory consumption less than 4.3 GB in all test experiments; also, it was observed that this approach also has dynamic growth in the structure plus the no-loss of its metric nature which gives it an advantage over graph-based representations.

In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features—point and line segments—have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.

The paper presents a robust outdoor stereo vision simultaneous localization and mapping (SLAM) algorithm. It estimates camera pose reliably in outdoor environments with directional sunlight illumination causing shadows and non-uniform scene lighting. The algorithm has been developed to measure a mining rope shovel’s rotation angle about its vertical axis (“swing” axis). A stereo camera is mounted externally to the shovel house (upper revolvable portion of the shovel), with a clear view of the shovel’s lower carbody. As the shovel house swings, the camera revolves with the shovel house in a planar circular orbit, seeing differing views of the carbody top. During the swing, the SLAM algorithm builds a map of observed 3D features on the carbody and simultaneously using these landmarks to estimate the camera position. This estimated camera position is then used to compute the shovel swing angle. Two novel techniques are employed to improve the SLAM algorithm’s robustness in outdoor environments. First, a “Locally Maximal” feature selection technique for Harris corners is used to select features more consistently in non-uniformly illuminated scenes. Another novel technique is the use of 3D “Feature Clusters” as SLAM landmarks rather than individual single features. The Feature Cluster landmarks improve the robustness of the landmark matching and allow significant reduction of the SLAM filter computational cost. This approach of estimating the shovel swing angle has a maximum error of ±1° upon SLAM map convergence. Results demonstrate the improvements of using the novel techniques compared to previous methods.

The ability of intelligent unmanned platforms to achieve autonomous navigation and positioning in a large-scale environment has become increasingly demanding, in which LIDAR-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. However, the LIDAR-based SLAM system will degenerate and affect the localization and mapping effects in extreme environments with high dynamics or sparse features. In recent years, a large number of LIDAR-based multi-sensor fusion SLAM works have emerged in order to obtain a more stable and robust system. In this work, the development process of LIDAR-based multi-sensor fusion SLAM and the latest research work are highlighted. After summarizing the basic idea of SLAM and the necessity of multi-sensor fusion, this paper introduces the basic principles and recent work of multi-sensor fusion in detail from four aspects based on the types of fused sensors and data coupling methods. Meanwhile, we review some SLAM datasets and compare the performance of five open-source algorithms using the UrbanNav dataset. Finally, the development trend and popular research directions of SLAM based on 3D LIDAR multi-sensor fusion are discussed and summarized.

Building maps is one of the most fundamental tasks for an autonomous robot. This robot should be able to construct a map of the environment and, at the same time, localize itself in it. This problem, known as Simultaneous Localization and Mapping (SLAM), has received great interest in the last years (Leonard & Durrant-Whyte, 1991). In our particular case, the robots build their maps using the FastSLAM algorithm (Montemerlo et al., 2002). The main idea of the FastSLAM algorithm is to separate the two fundamental aspects of the SLAM problem: the estimate of the robot’s pose and the estimate of the map. This algorithm uses a particle set that represents the uncertainty of the robot’s pose (localization problem) meanwhile each particle has its own associated map (several individual estimates of the landmarks conditioned to the robot’s path). The solution to the SLAM problem is performed by means of a sampling and particle generation process, in which the particles whose current observations do not fit with their associated map are eliminated. The FastSLAM algorithm has proved to be robust to false data association and it is able to represent models of non-linear movements in a reliable way (Howard, 2006). In relation to the sensors used to build the maps, many authors use range sensors such as sonar (Kwak et al., 2008; Wijk & Christensen, 2000) or laser (Thrun, 2004; Triebel & Burgard, 2005). Nevertheless, there is an increasing interest on using cameras as sensors. This approach is denoted as visual SLAM (Valls Miro et al., 2006). These devices obtain a higher amount of information from the environment and they are less expensive than laser as well. Furthermore, 3D information can be obtained when using stereo cameras. These are the sensors used in this work. Most visual SLAM approaches are landmark-based. These landmarks consist of a set of distinctive points which are referred to a global reference system. The main advantage of landmark-based maps is the compactness of their representation. By contrast, this kind of maps requires the existence of structures or objects that are distinguishable enough. The map building problem can be solved by a single robot (Moutalier & Chatila, 1989), but it will be more efficiently managed if there is a team of robots, which collaborates in the construction of the map (Howard, 2006). In this case, the space can be divided so that the distances traversed are shorter and thus the odometry errors will be smaller. In this work, 7

Existing Simultaneous Localization and Mapping (SLAM) approaches are limited in their scalability due to growing map size in long-term robot operation. Moreover, processing such maps for localization and planning tasks leads to the increased computational resources required onboard. To address the problem of memory consumption in long-term operation, we develop a novel real-time SLAM algorithm, MeSLAM, that is based on neural field implicit map representation. It combines the proposed global mapping strategy, including neural networks distribution and region tracking, with an external odometry system. As a result, the algorithm is able to efficiently train multiple networks representing different map regions and track poses accurately in large-scale environments. Experimental results show that the accuracy of the proposed approach is comparable to the state-of-the-art methods (on average, 6.6 cm on TUM RGB-D sequences) and outperforms the baseline, iMAP*. Moreover, the proposed SLAM approach provides the most compact-sized maps without details distortion (1.9 MB to store 57 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) among the state-of-the-art SLAM approaches.

The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous localization and mapping (SLAM) technology is a hot spot in solving the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, and 0.6 m/s, respectively; the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the SD is less than 6 cm; the average course deviation is <3°, and the SD is <1.5°. Both the positioning accuracy and the navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.

Mapping is one of the mobile robot's most basic applications. A mobile robot's sensors, such as a laser sensor, sonar, and camera, are used to create the map. Most mapping techniques use simultaneous localization and Mapping (SLAM). SLAM allows for creating a map and the localization of the robot's position on it. This research compares the trajectories of a mobile robot created by several ROS-based SLAM systems. And also, GMapping and Karto SLAM are two well-known SLAM algorithms employed. The mobile robot is equipped with 2D lidar and monocular camera. The mapping is done at two distinct locations, in labs of varying sizes with varying numbers of static and dynamic objects. Three test runs are conducted for GMapping to examine the effects of various variables on mapping quality, including particle filter, mapping delay, and robot speed. The results show a significant difference in operation completion time and mapping accuracy as a result of the parameter changing over the three test runs. Due to the improved accuracy of the parameter used in the second test run of GMapping and Karto SLAM, the accuracy of the maps is the basis for this improvement. On the other hand, the second test run with robot particle filter 30, mapping delay 1, and speed 0.13m/s is thought to be the best3\2 13Q/0.

The two-dimensional (2D) lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the environment. We propose a robust 2D lidar simultaneous localization and mapping (SLAM) algorithm working in ambiguous environments. To improve the front-end scan-matching module’s accuracy and robustness, we propose performing degeneration analysis, line landmark tracking, and environment coverage analysis. The max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous environment. Moreover, we propose a tightly coupled framework that integrates lidar, wheel odometer, and inertial measurement unit (IMU). The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM problem. The experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8% of the existing method. And it can run stably in ambiguous environments where the existing method will be failed. Moreover, it ccan successfully construct a map with an area of more than 250 000 square meters.

Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, we present a keyframe-based approach to visual-inertial simultaneous localization and mapping (SLAM) for monocular and stereo cameras. Our visual-inertial SLAM system is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates. We achieve global consistency in the estimate through online loop-closing and non-linear optimization. Furthermore, our system supports relocalization in a map that has been previously obtained and allows for continued SLAM operation. We evaluate our approach in terms of accuracy, relocalization capability and run-time efficiency on public indoor benchmark datasets and on newly recorded outdoor sequences. We demonstrate state-of-the-art performance of our system compared to a visual-inertial odometry method and baseline visual SLAM approaches in recovering the trajectory of the camera.

This paper presents a practical approach to solve the simultaneous localization and mapping (SLAM) problem for autonomous mobile platforms by using natural visual landmarks obtained from an stereoscopic camera. It is an attempt to depart from traditional sensors such as laser rangefinders in order to gain the many benefits of nature-inspired information-rich 3D vision sensors. Whilst this makes the system fully observable in that the sensor provide enough information (range and bearing) to compute the full 2D estate of the observed landmarks from a single position, it is also true that depth information is difficult to rely on, particularly on measurements beyond a few meters (in fact the full 3D estate is observable, but here robot motion is constrained to 2D and only the 2D problem is considered). The work presented here is an attempt to overcome such a drawback by tackling the problem from a partially measurable SLAM perspective in that only landmark bearing from one of the cameras is employed in the fusion estimation. Range information estimates from the stereo pair is only used during map building in the landmark initialization phase in order to provide a reasonably accurate initial estimate. An additional benefit of the approach presented here lies in the data association aspect of SLAM. The availability of powerful feature extraction algorithms from the vision community, such as SIFT, permits a more flexible SLAM implementation separated from feature representation, extraction and matching, essentially carrying out matching with minimal recourse to geometry.

This thesis is about feature-based mobile robot navigation in unknown environments. The work is focusing on solving the problem of Simultaneous Localization and Mapping (SLAM) including feature extraction, estimation and complete map building solution. The robot platforms used in this work have the encoders for odometry and several laser scanners for exteroperceptive sensors. Simulations, real experiments and existing well known datasets are used in order to verify the performance of the developed algorithms. The algorithms developed in this thesis are features based meaning that instead of directly using raw sensory data geometric features are first extracted. This allows the filtering of noise from the sensors and the dynamics of the environment. More importantly, feature based approaches can reduce the amount of data needed to process, enabling the algorithms to scale in large environments. Another advantage of using features is the characteristic of better isolating informative patterns so that by exploiting specific knowledge of the environment, specialized algorithms can be easily adopted with different types of features. Several geometric features (points, lines, planes) are considered in this work. In this thesis, the relative mapping approach is adopted for solving the SLAM problem. This approach has many advantages. First, the mapping is decoupled from the estimation of robot pose so that the map estimation is independent from odometry errors. In other words, odometry errors do not affect and accumulate to the map estimate as in the absolute mapping approach. Second, the estimation does not involve the dynamics model of the robot which is identity in a relative mapping algorithm, meaning that only the observation model is considered in the estimation. A relative map has extra consistency problem compared to an absolute map. It is subject to the geometrical consistency problem which may generate an inconsistent absolute map after an transformation. However, having only observation model in the estimation gives an insight to separating nonlinearities from the iterative part of the estimator and thus minimize the effects of linearization errors. In this work, these subjects are investigated and reported. For some service robotic applications, especially for indoor office-like environments, we would like to have a fast, robust and lightweight approach which is capable to perform real-time SLAM on minimal hardware platforms. By exploiting some known knowledge about the structure of the environment, we claim to be able to achieve that goal. Particularly, the OrthoSLAM algorithm has been developed for building 2D and 3D maps. The algorithm adopts the Orthogonality assumption (or constraint) on the environment. First, the complexity of the SLAM algorithm is significantly reduced. Second, the linearization error from the robot heading uncertainty is removed and thus the map consistency is greatly improved. Implementations of the developed algorithms are tested on simulated data, on existing real world datasets and on robot platforms. Existing algorithms used in this work have been also reimplemented. The performance of the algorithms are analysed and compared on well chosen criteria. Particularly, the issues on computational complexity, linearization error, map consistency, map precision, realtime capability are investigated.

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.

Simultaneous localization and mapping (SLAM) is a pivotal technology in autonomous vehicle navigation and a significant research area in robotics. Addressing the inaccuracies in point cloud registration of traditional LiDAR SLAM, which lead to localization and mapping errors, we propose a novel LiDAR inertial odometry approach integrating inertial measurement units and a multi-feature joint registration strategy. Initially, we introduce an innovative ground segmentation method and feature categorization strategy, enhancing ground detection mechanisms and optimizing the feature extraction process. Subsequently, our multi-feature joint registration method computes the pose transformations between current frames and the local map. Finally, we employ a global registration method based on fast point feature histograms feature descriptors for coarse alignment, providing initial estimates for the generalized iterative closest point algorithm, thus efficiently and accurately mitigating cumulative errors. Extensive evaluations on the KITTI dataset and real-world campus environment demonstrate that our approach significantly surpasses existing advanced LiDAR SLAM solutions, achieving over a 24% improvement in pose estimation accuracy.