Robot path planning using SIFT and sonar sensor fusion

Authors:
Alfredo Chávez;Hector Raposo
Affiliations:
Aalborg University, Department of Control, Aalborg, Denmark;Aalborg University, Department of Control, Aalborg, Denmark
Venue:
ROCOM'07 Proceedings of the 7th WSEAS International Conference on Robotics, Control & Manufacturing Technology
Year:
2007

Citing 4
Cited 1

Computational principles of mobile robotics

Computational principles of mobile robotics
Improved Occupancy Grids for Map Building

Autonomous Robots
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Robust data fusion with occupancy grid

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Exploration with two cooperating mobile robots

WSEAS Transactions on Systems and Control

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Abstract

This paper presents a novel map building approach for path planning purposes, which takes into account the uncertainty inherent in sensor measurements. To this end, Bayesian estimation and Dempster-Shafer evidential theory are used to fuse the sensory information and to update the occupancy and evidential grid maps, respectively. The approach is illustrated using actual measurements from a laboratory robot. The sensory information is obtained from a sonar array and the Scale Invariant Feature Transform (SIFT) algorithm. Finally, the resulting two evidential maps based on Bayes and Dempster theories are used for path planning using the potential field method. Both yield satisfying results.