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Terrain-Adaptive Obstacle Detection
Benjamin Suger, Bastian Steder, Wolfram Burgard IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016
suger16iros.pdf
Notes: Reliable detection and avoidance of obstacles is
a crucial prerequisite for autonomously navigating robots as
both guarantee safety and mobility. To ensure safe mobility, the
obstacle detection needs to run online, thereby taking limited
resources of autonomous systems into account. At the same
time, robust obstacle detection is highly important. Here, a
too conservative approach might restrict the mobility of the
robot, while a more reckless one might harm the robot or
the environment it is operating in. In this paper, we present a
terrain-adaptive approach to obstacle detection that relies on
3D-Lidar data and combines computationally cheap and fast
geometric features, like step height and steepness, which are
updated with the frequency of the lidar sensor, with semantic
terrain information, which is updated with at lower frequency.
We provide experiments in which we evaluate our approach
on a real robot on an autonomous run over several kilometers
containing different terrain types. The experiments demonstrate
that our approach is suitable for autonomous systems that have
to navigate reliable on different terrain types including concrete,
dirt roads and grass.
BibTeX:
@inproceedings{suger16iros,
author = {Benjamin Suger and Bastian Steder and Wolfram Burgard},
title = {Terrain-Adaptive Obstacle Detection},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = 2016,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/suger16iros.pdf},
address = {Daejeon, Korea}
}
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