Vision-Based Obstacle Avoidance Using SIFT Features

Authors:
Aaron Chavez;David Gustafson
Affiliations:
Department of Computer Science, Kansas State University, Manhattan 66506;Department of Computer Science, Kansas State University, Manhattan 66506
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Year:
2009

Citing 9
Cited 0

Real-time obstacle avoidance for manipulators and mobile robots

International Journal of Robotics Research
Performance of optical flow techniques

International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms

International Journal of Computer Vision
High-Speed Obstacle Avoidance and Self-Localization for Mobile Robots Based on Omni-directional Imaging of Floor Region

RoboCup 2001: Robot Soccer World Cup V
Appearance-Based Obstacle Detection with Monocular Color Vision

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Real-Time Single-Workstation Obstacle Avoidance Using Only Wide-Field Flow Divergence

ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
High speed obstacle avoidance using monocular vision and reinforcement learning

ICML '05 Proceedings of the 22nd international conference on Machine learning
A vision based system for goal-directed obstacle avoidance

RoboCup 2004

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a vision-based collision detection algorithm. Our approach is similar to optic flow-based approaches, except that we are working at a feature level instead of a pixel level. The algorithm analyzes a pair of images taken from a moving camera at different times. Then, it recognizes imminent collisions by analyzing the change in scale and location of SIFT features in the pair of images. We have evaluated the performance of this algorithm and present our experimental results.