On the analysis of the depth error on the road plane for monocular vision-based robot navigation

Dezhen Song, Hyunnam Lee, Jingang Yi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A mobile robot equipped with a single camera can take images at different locations to obtain the 3D information of the environment for navigation. The depth information perceived by the robot is critical for obstacle avoidance. Given a calibrated camera, the accuracy of depth computation largely depends on locations where images have been taken. For any given image pair, the depth error in regions close to the camera baseline can be excessively large or even infinite due to the degeneracy introduced by the triangulation in depth computation. Unfortunately, this region often overlaps with the robot's moving direction, which could lead to collisions. To deal with the issue, we analyze depth computation and propose a predictive depth error model as a function of motion parameters. We name the region where the depth error is above a given threshold as an untrusted area. Note that the robot needs to know how its motion affect depth error distribution beforehand, we propose a closed-form model predicting how the untrusted area is distributed on the road plane for given robot/camera positions. The analytical results have been successfully verified in the experiments using a mobile robot.

Original languageEnglish (US)
Title of host publicationAlgorithmic Foundations of Robotics VIII - Selected Contributions of the Eighth International Workshop on the Algorithmic Foundations of Robotics
Pages301-315
Number of pages15
DOIs
StatePublished - Mar 29 2010
Event8th International Workshop on the Algorithmic Foundations of Robotics, WAFR - Guanajuato, Mexico
Duration: Dec 7 2008Dec 9 2008

Publication series

NameSpringer Tracts in Advanced Robotics
Volume57
ISSN (Print)1610-7438
ISSN (Electronic)1610-742X

Other

Other8th International Workshop on the Algorithmic Foundations of Robotics, WAFR
CountryMexico
CityGuanajuato
Period12/7/0812/9/08

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Artificial Intelligence

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