PhD Thesis Poppinga: RRT - Jacobs University

Abstract
Autonomous navigation is an important challenge in many application fields of mobile robotics. This
thesis contributes to mobile robot autonomous navigation at different levels. In chapter 2, different
methods for 3D sensing are discussed. In particular, we evaluate a time-of-flight range camera in
various situations. We develop a method to detect and, if desirable, heuristically correct the most
inhibitive error source: Range is detected via phase shift. Because of the way phase shift is measured,
values beyond 360° will be mapped back into the [0°, 360°[ interval. The derived range is consequently
also affected. The error is inherent not only to this model, but to all range cameras measuring
the time-of-flight in the same way. Our correction makes it possible to use the range camera on a
mobile robot for high-frequency 3D sensing. In chapter 3, we present several 3D point cloud datasets
gathered with the time-of-flight camera presented in the preceding chapter and other 3D sensors: an
actuated laser range-finder, a stereo camera, and a 3D sonar. The datasets represent a wide variety of
real-world and set-up scenes, indoors and outdoors, underwater and land, and small scale and large
scale. These datasets are used in the experiments in the following chapters. In chapter 4, we present a
robust short-range obstacle detection algorithm that runs fast enough to actually make use of the range
camera’s high data rate. It is based on the Hough transform for planes in 3D point clouds. The bins are
relatively coarse which turned out to be enough for testing for drivability. The method allows a mobile
robot to reliably detect the drivability of the terrain it faces. With the same method and finer bins it
is possible to classifiy the terrain, albeit not as reliably as checking drivability. Experiments with two
types of sensors on data from indoors and outdoors demonstrated the algorithm’s performance. The
processing time typically lies between 5 and 50 ms. This is enough for real-time processing on a robot
moving at reasonable speed. In chapter 5, we develop the Patch Map data-structure for memory efficient
3D mapping based on planar surfaces extracted from 3D point cloud data. It is flexible enough
to allow for different kind of surface representations, different methods of collision detection, and different
kinds of roadmap algorithms. Surface representations are planar polygons and trimeshes. We
survey and benchmark different methods of generating planar patches from a point cloud segmented
into planar regions. We also implement a point cloud based variant of the map data-structure that
allows for comparison with this standard data-structure. Collision detection is implemented in a way
to exploit the fact that planar patches are polygons and also based on two external collision detection
libraries, Bullet and OPCODE. The implemented roadmap algorithms are Rapidly-exploring Random
Tree (RRT), Probabilistic Roadmap Method (PRM) and variants of these, most notably variants of
RRT and PRM that place vertices on the medial axes of the map without explicitly computing it. We
benchmark all collision detection methods with all roadmap algorithms on synthetic data to find out
the most efficient ones. RRT is the best roadmap algorithm and Bullet the fastest collision detection.
Thus, the Patch map data-structure shows its flexibility and usability in practice. In chapter 6, we
thoroughly test the Patch Map data-structure developed in the preceding chapter on real-world data.
The Patch Map consisting only of planar patches is 18 times smaller than the point clouds it is based
on. We perform both roadmap generation with PRM and RRT and way finding from start to goal,
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