Monocular Vision Based Obstacle Detection for Robot Navigation in Unstructured Environment

Authors:
Yehu Shen;Xin Du;Jilin Liu
Affiliations:
Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang, P.R. China;Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang, P.R. China;Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 7
Cited 0

VITS-A Vision System for Autonomous Land Vehicle Navigation

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Obstacle Avoidance Using Flow Field Divergence

IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for road navigation

Vision-based vehicle guidance
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Mars Rover Autonomous Navigation

Autonomous Robots
Real-time obstacle avoidance using central flow divergence and peripheral flow

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A probabilistic framework for correspondence and egomotion

WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an algorithm to detect the obstacles in outdoor unstructured environment with monocular vision. It makes use of motion cues in the video streams. Firstly, optical flow at feature points is calculated. Then rotation of the camera and FOE(focal of expansion) are evaluated separately. A non-linear optimization method is adopted to refine the rotation and FOE. Finally, we get inverse TTC(time to contact) with rotation and FOE and detect the obstacles in the scene. The algorithm doesn't need any assumption that the ground is flat or partially flat as the conventional methods. So it is suitable for outdoor unstructured environment. Qualitative and quantitative experiment results show that our algorithm works well on different kinds of terrains.