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This thesis addresses the problem of terrain classification in unstructured outdoor environments. Terrain classification includes the detection of obstacles and passable areas as well as the analysis of ground surfaces. A 3D laser range finder is used as primary sensor for perceiving the surroundings of the robot. First of all, a grid structure is introduced for data reduction. The chosen data representation allows for multi-sensor integration, e.g., cameras for color and texture information or further laser range finders for improved data density. Subsequently, features are computed for each terrain cell within the grid. Classification is performedrnwith a Markov random field for context-sensitivity and to compensate for sensor noise and varying data density within the grid. A Gibbs sampler is used for optimization and is parallelized on the CPU and GPU in order to achieve real-time performance. Dynamic obstacles are detected and tracked using different state-of-the-art approaches. The resulting information - where other traffic participants move and are going to move to - is used to perform inference in regions where the terrain surface is partially or completely invisible for the sensors. Algorithms are tested and validated on different autonomous robot platforms and the evaluation is carried out with human-annotated ground truth maps of millions of measurements. The terrain classification approach of this thesis proved reliable in all real-time scenarios and domains and yielded new insights. Furthermore, if combined with a path planning algorithm, it enables full autonomy for all kinds of wheeled outdoor robots in natural outdoor environments.
In current research of the autonomous mobile robots, path planning is still a very important issue.
This master's thesis deals with various path planning algorithms for the navigation of such mobile systems. This is not only to determine a collision-free trajectory from one point to another. The path should still be optimal and comply with all vehicle-given constraints. Especially the autonomous driving in an unknown and dynamic environment poses a major challenge, because a closed-loop control is necessary and thus a certain dynamic of the planner is demanded.
In this paper, two types of algorithms are presented. First, the path planner, based on A*, which is a common graph search algorithm: A*, Anytime Repairing A*, Lifelong Planning A*, D* Lite, Field D*, hybrid A*. Second, the algorithms which are based on the probabilistic planning algorithm Rapidly-exploring Random Tree (Rapidly-exploring Random Tree, RRT*, Lifelong Planning RRT*), as well as some extensions and heuristics. In addition, methods for collision avoidance and path smoothing are presented. Finally, these different algorithms are evaluated and compared with each other.
In this thesis we present an approach to track a RGB-D camera in 6DOF andconstruct 3D maps. We first acquire, register and synchronize RGB and depth images. After preprocessing we extract FAST features and match them between two consecutive frames. By depth projection we regain the z-value for the inlier correspondences. Afterwards we estimate the camera motion by 3D point set alignment between the correspondence set using least-squares. This local motion estimate is incrementally applied to a global transformation. Additionally wernpresent methods to build maps based on point cloud data acquired by a RGB-D camera. For map creation we use the OctoMap framework and optionally create a colored point cloud map. The system is evaluated with the widespread RGB-D benchmark.