25 October 2004 Processing real-time stereo video for an autonomous robot using disparity maps and sensor fusion
Author Affiliations +
Abstract
The Bearcat “Cub” Robot is an interactive, intelligent, Autonomous Guided Vehicle (AGV) designed to serve in unstructured environments. Recent advances in computer stereo vision algorithms that produce quality disparity and the availability of low cost high speed camera systems have simplified many of tasks associated with robot navigation and obstacle avoidance using stereo vision. Leveraging these benefits, this paper describes a novel method for autonomous navigation and obstacle avoidance currently being implemented on the UC Bearcat Robot. The core of this approach is the synthesis of multiple sources of real-time data including stereo image disparity maps, tilt sensor data, and LADAR data with standard contour, edge, color, and line detection methods to provide robust and intelligent obstacle avoidance. An algorithm is presented with Matlab code to process the disparity maps to rapidly produce obstacle size and location information in a simple format, and features cancellation of noise and correction for pitch and roll. The vision and control computers are clustered with the Parallel Virtual Machine (PVM) software. The significance of this work is in presenting the methods needed for real time navigation and obstacle avoidance for intelligent autonomous robots.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Donald W. Rosselot, Donald W. Rosselot, Ernest L. Hall, Ernest L. Hall, } "Processing real-time stereo video for an autonomous robot using disparity maps and sensor fusion", Proc. SPIE 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, (25 October 2004); doi: 10.1117/12.571291; https://doi.org/10.1117/12.571291
PROCEEDINGS
9 PAGES


SHARE
Back to Top