A Real-time relative probabilistic mapping algorithm for high-speed off-road autonomous driving
Cheng Chen, Yuqing He, Feng Gu, Chunguang Bu, Jianda Han
Abstract
Reliable mapping and hazard detection are prerequisites for autonomous navigation for unmanned ground vehicles. Because of the uncertainty and vibration induced by high-speed navigation and rugged terrain, the problem of mapping for high-speed off-road autonomous navigation has not been completely solved yet. A relative probabilistic mapping (RPM) algorithm is introduced to address the problem. Firstly, the relative probabilistic map is updated by Kalman filter and Gaussian Mixture algorithm based on the probabilistic exteroceptive measurements model. Then, terrain traversability is evaluated to identify obstacles in the map. Experiments on off-road high-speed autonomous vehicle, which suffers from severe vibration, with different sensor configurations are carried out to demonstrate the capability of the RPM algorithm.
BibTeX
@inproceedings{iros2015_arealtimerelativ,
title = {A Real-time relative probabilistic mapping algorithm for high-speed off-road autonomous driving},
author = {Cheng Chen and Yuqing He and Feng Gu and Chunguang Bu and Jianda Han},
booktitle = {IROS 2015},
year = {2015}
}