Authors:
Kazuma Haraguchi
1
;
Jun Miura
1
;
Nobutaka Shimada
2
and
Yoshiaki Shirai
2
Affiliations:
1
Graduate School of Engineering, Osaka University, Japan
;
2
Faculty of Science Engineering, Ritsumeikan University, Japan
Keyword(s):
Obstacle Map Generation, Bayes Theorem, Occlusion, Spatial Dependency, Visibility.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
This paper describes a method of probabilistic obstacle map building based on Bayesian estimation. Most active or passive obstacle sensors observe only the most frontal objects and any objects behind them are occluded. Since the observation of distant places includes large depth errors, conventional methods which does not consider the sensor occlusion often generate erroneous maps. We introduce a probabilistic observation model which determines the visible objects. We first estimate probabilistic visibility from the current view-point by a Markov chain model based on the knowledge of the average sizes of obstacles and free areas. Then the likelihood of the observations based on the probabilistic visibility are estimated and then the posterior probability of each map grid are updated by Bayesian update rule. Experimental results show that more precise map building can be bult by this method.