Learning long-range terrain classification for autonomous navigation

被引：18

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

页码：4018 / 4024

页数：7

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