Daun, Kevin (2017)
Robust 3D SLAM for Mobile Search and Rescue Robots in Challenging Environments.
Technische Universität Darmstadt
Masterarbeit, Bibliographie
Kurzbeschreibung (Abstract)
Rescue robot systems operate in highly challenging environments characterized by rough, possibly unknown terrain leading to non-smooth robot motions and poor odometry estimates. To enable autonomous and assisted operation, rescue robots have to localize themselves within the environment and create a map of it by employing a SLAM (Simultaneous Localization and Mapping) approach. Performing 3D laser rangefinder SLAM requires comprehensive sensor coverage of 3D space. As state- of-the-art laser rangefinders only scan a planar slice of the environment, additional motion of the sensor relative to the robot is necessary to capture a 3D scan. Thereby, separate sections of the 3D scan are captured at different times yielding distortions in the 3D scan when the robot is moving. Based on an analysis of existing approaches, this work builds on top of the Google Cartographer approach. Cartographer combines occupancy grid based scan matching with sensor observations of IMU and odometry in a joint optimization problem to resolve the scan distortion. As scan matching accumulates error, the system maintains a pose graph to perform loop closure and reduce the error of the trajectory. For 2D laser rangefinder SLAM, representing the map with Truncated Signed Distance Fields has shown to improve the accuracy of the pose estimate and the map. To allow an exchange of the map representation, a generalized formulation for generic grid maps is proposed. As specific instances of generic grid maps, occupancy grids, Truncated Signed Distance Fields and Euclidean Signed Distance Fields are ana- lyzed. Furthermore, the alignment of the robot trajectory against external references such as the Global Navigation Satellite System is examined. In the experiments comparing the grid map instances for scan matching, the signed distance field (SDF) approaches improve the accuracy of scan matching and thus the resulting trajectory and map. Furthermore, the SDF approaches yield a reduced number of iterations for the optimizations to converge leading to faster scan matching. However, the SDF approaches increase the map update time by an order of magnitude. The approach is evaluated in multiple scenarios showing the capability to handle highly challenging terrain with the corresponding incorrect odometry and high angular accelerations as shown in a scenario from the Robocup 2017 competition. The large scale behavior is validated on a ~ 500 m loop around a building in a dynamic environment. The proposed method detects the loop closure and corrects for the accumulated error.
| Typ des Eintrags: | Masterarbeit |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Daun, Kevin |
| Art des Eintrags: | Bibliographie |
| Titel: | Robust 3D SLAM for Mobile Search and Rescue Robots in Challenging Environments |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Ort: | Darmstadt |
| Kurzbeschreibung (Abstract): | Rescue robot systems operate in highly challenging environments characterized by rough, possibly unknown terrain leading to non-smooth robot motions and poor odometry estimates. To enable autonomous and assisted operation, rescue robots have to localize themselves within the environment and create a map of it by employing a SLAM (Simultaneous Localization and Mapping) approach. Performing 3D laser rangefinder SLAM requires comprehensive sensor coverage of 3D space. As state- of-the-art laser rangefinders only scan a planar slice of the environment, additional motion of the sensor relative to the robot is necessary to capture a 3D scan. Thereby, separate sections of the 3D scan are captured at different times yielding distortions in the 3D scan when the robot is moving. Based on an analysis of existing approaches, this work builds on top of the Google Cartographer approach. Cartographer combines occupancy grid based scan matching with sensor observations of IMU and odometry in a joint optimization problem to resolve the scan distortion. As scan matching accumulates error, the system maintains a pose graph to perform loop closure and reduce the error of the trajectory. For 2D laser rangefinder SLAM, representing the map with Truncated Signed Distance Fields has shown to improve the accuracy of the pose estimate and the map. To allow an exchange of the map representation, a generalized formulation for generic grid maps is proposed. As specific instances of generic grid maps, occupancy grids, Truncated Signed Distance Fields and Euclidean Signed Distance Fields are ana- lyzed. Furthermore, the alignment of the robot trajectory against external references such as the Global Navigation Satellite System is examined. In the experiments comparing the grid map instances for scan matching, the signed distance field (SDF) approaches improve the accuracy of scan matching and thus the resulting trajectory and map. Furthermore, the SDF approaches yield a reduced number of iterations for the optimizations to converge leading to faster scan matching. However, the SDF approaches increase the map update time by an order of magnitude. The approach is evaluated in multiple scenarios showing the capability to handle highly challenging terrain with the corresponding incorrect odometry and high angular accelerations as shown in a scenario from the Robocup 2017 competition. The large scale behavior is validated on a ~ 500 m loop around a building in a dynamic environment. The proposed method detects the loop closure and corrects for the accumulated error. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik |
| Hinterlegungsdatum: | 06 Jun 2019 06:03 |
| Letzte Änderung: | 06 Jun 2019 06:03 |
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