IROS 2015 Paper Abstract


Paper ThFT8.2

Chen, Cheng (Shenyang Institute of Automation, Chinese Academy of Sciences), He, Yuqing (Shenyang Institute of Automation, Chinese Academy of Sciences), Gu, Feng (Shenyang Institute of Automation, CAS), Bu, Chunguang (Shenyang Institute of Automation, Chinese Academy of Sciences), Han, Jianda (Shenyang Institute of Automation, Chinese AcademyofSciences)

A Real-Time Relative Probabilistic Mapping Algorithm for High-Speed Off-Road Autonomous Driving

Scheduled for presentation during the Regular session "Mapping 4" (ThFT8), Thursday, October 1, 2015, 17:05−17:20, Saal F

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 19, 2019

Keywords Mapping, Intelligent Transportation Systems, Field Robots


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.



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