Learning long-range terrain classification for autonomous navigation

被引：17
|
作者：
Bajracharya, Max [1 ]
Tang, Benyang [1 ]
Howard, Andrew [1 ]
Turmon, Michael [1 ]
Matthies, Larry [1 ]
机构：
[1] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA
来源：
2008 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-9 | 2008年
关键词：
D O I：
10.1109/ROBOT.2008.4543828
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper describes a method for learning the terrain classification of long-range appearance data from short-range, stereo-based geometry, along with a map representation for utilizing this data to improve autonomous off-road navigation. The continuous, online learning method allows the system to constantly adapt to changing terrain and environmental conditions, while the polar-perspective map representation allows the system to effectively plan with stereo data at long ranges. Various evaluations of the long-range classification and improvements in system performance are described, including results from an independent third-party testing team.
引用
收藏
页码：4018 / 4024
页数：7
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