Graph-based Robust Localization and Mapping for Autonomous Mobile Robotic Navigation

Simultaneous Localization and Mapping (SLAM) aims to estimate the positions and orientations of the mobile robot and to construct the model of the environment. SLAM can help the robot to plan and execute a collision-free trajectory from the current configuration to the target configuration, so it is essential and critical for the mobile robot’s autonomous navigation and effective task execution. SLAM plays a quite important role in a wide range of application fields, from indoor to outdoor, from industry to military, from terrain, submarine to outer space, etc. In indoor dynamic scenarios where there are moving objects, robust SLAM is also important for the mobile robot to co-exist with humans safely and to improve the capability in robot’s estimation for its own state of pose and the surrounding world model. The research goal of this dissertation is to design, implement and validate Graph-SLAM algorithms for mobile robots in indoor office-like dynamic scenarios. Graph-SLAM belongs to the category that addresses the issues of localization and mapping in a hierarchical way, where a topological graph is constructed to represent the robot poses, the local relative motion constraints between them are estimated as the edges of the graph and the global consistent registration is performed to estimate the trajectory of the robot. Graph-SLAM can lead to much accurate results approaching the ground truth. The overview of the designed and implemented Graph-SLAM algorithm includes the following three parts: scan matching to estimate the local relative roto-translation, batch optimization to estimate the global mobile robot’s trajectory, and the global line-feature-based mapping to construct the global line-feature model of the environment. The details of each chapter are briefly shown as follows: On the local level, moving-object-detection based scan matching is accomplished: first, conditioned-Hough-Transform-based segmentation is performed to extract and group the small-scale line-feature-candidate samples; second, occupancy-analysis-based moving-object detection is executed to detect and discard the segments corresponding to the moving objects; third, linear-regression-based line-feature matching is applied to merge the similar small-scale line features into larger-scale line features, and also to match the larger-scale line features in order to estimate the roto-translation values. The experiments will prove the effectiveness of the algorithm to estimate the relative roto-translation value even faced with the disturbances of the moving objects in the dynamic scenario. On the global level, the motion constraints computed from scan matching between the immediate consecutive, the close-by-but-not-adjacent robot poses are used to construct the topological graph, and the least-square cost function associated with the graph is optimized by a linear solution. The experimental tests dealing with the publicly available dataset will prove the effectiveness of the batch optimization method, which is quite efficient and accurate. In addition, for the local-level relative roto-translation estimation, yet-another robust wall-detection-based scan-matching algorithm is proposed and implemented to enhance the capability of the previous scan-matching algorithm: first, conditioned-Hough-Transform-and-linear-regression-based line-segment detection is performed to detect the line segments from the raw laser-scan-range data; second, wall detection is done to select the line segments that correspond to the walls of the environment; third, matching by fitting point to line is executed to estimate the roto-translation value. The experimental result will verify the effectiveness of this algorithm even when the moving object is close to the wall and there is much rotation error in the input odometry data. Moreover, on the global level, with the knowledge of the estimated global robot poses for the transformations between the local robot frames and the global inertial frame, the local line-feature maps can be transformed and integrated to the global frame in order to construct the global line-feature-based map. The experimental verification will prove the effectiveness of the complete graph- based robust and mapping mapping algorithm both in simulation and in actual large-dataset hardware experiment. In conclusion, one hierarchical robust graph-based localization and mapping algorithm is designed and implemented in this dissertation for dynamic indoor scenarios, which solves the problem of localization and mapping in a robust and effective way. One possible future direction for the research is to adapt this full off-line graph-based SLAM approach to the on-line version to deal with live sensor data