A terrain description method for traversability analysis based on elevation grid map

Terrain traversability analysis is a challenging problem for mobile robots to adapt to complex environments, including the detection of cluttered obstacles, potholes, or even slopes. With the accurate distance information, using distance sensors such as three-dimensional light detection and ranging...

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Main Authors: Xiangrui Meng, Zhiqiang Cao, Shuang Liang, Lei Pang, Shuo Wang, Chao Zhou
Format: Article
Language:English
Published: SAGE Publishing 2018-01-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/1729881417751530
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spelling doaj-6db3604be8c34db8a04b8f8f0c13dbae2020-11-25T03:15:32ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142018-01-011510.1177/1729881417751530A terrain description method for traversability analysis based on elevation grid mapXiangrui Meng0Zhiqiang Cao1Shuang Liang2Lei Pang3Shuo Wang4Chao Zhou5 University of Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, ChinaTerrain traversability analysis is a challenging problem for mobile robots to adapt to complex environments, including the detection of cluttered obstacles, potholes, or even slopes. With the accurate distance information, using distance sensors such as three-dimensional light detection and ranging (LiDAR) for terrain description becomes a preferred choice. In this article, a terrain description method for traversability analysis based on elevation grid map is presented. After the elevation grid map is generated, the ground is segmented with the aid of a height difference kernel and the non-ground grids in the map are then clustered. The terrain description features, including height index, roughness, and slope angle, are calculated and estimated. The slope angle is estimated using random sample consensus (RANSAC) and least squares method, and specifically, the roughness is combined to eliminate false slopes. Experimental results verified the effectiveness of the proposed method.https://doi.org/10.1177/1729881417751530
collection DOAJ
language English
format Article
sources DOAJ
author Xiangrui Meng
Zhiqiang Cao
Shuang Liang
Lei Pang
Shuo Wang
Chao Zhou
spellingShingle Xiangrui Meng
Zhiqiang Cao
Shuang Liang
Lei Pang
Shuo Wang
Chao Zhou
A terrain description method for traversability analysis based on elevation grid map
International Journal of Advanced Robotic Systems
author_facet Xiangrui Meng
Zhiqiang Cao
Shuang Liang
Lei Pang
Shuo Wang
Chao Zhou
author_sort Xiangrui Meng
title A terrain description method for traversability analysis based on elevation grid map
title_short A terrain description method for traversability analysis based on elevation grid map
title_full A terrain description method for traversability analysis based on elevation grid map
title_fullStr A terrain description method for traversability analysis based on elevation grid map
title_full_unstemmed A terrain description method for traversability analysis based on elevation grid map
title_sort terrain description method for traversability analysis based on elevation grid map
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2018-01-01
description Terrain traversability analysis is a challenging problem for mobile robots to adapt to complex environments, including the detection of cluttered obstacles, potholes, or even slopes. With the accurate distance information, using distance sensors such as three-dimensional light detection and ranging (LiDAR) for terrain description becomes a preferred choice. In this article, a terrain description method for traversability analysis based on elevation grid map is presented. After the elevation grid map is generated, the ground is segmented with the aid of a height difference kernel and the non-ground grids in the map are then clustered. The terrain description features, including height index, roughness, and slope angle, are calculated and estimated. The slope angle is estimated using random sample consensus (RANSAC) and least squares method, and specifically, the roughness is combined to eliminate false slopes. Experimental results verified the effectiveness of the proposed method.
url https://doi.org/10.1177/1729881417751530
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