Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment
Johann Laconte, Christophe Debain, Roland Chapuis, François Pomerleau, Romuald Aufrère
Abstract
In a context of autonomous robots, one of the most important tasks is to ensure the safety of the robot and its surrounding. The risk of navigation is usually said to be the probability of collision. This notion of risk is not well defined in the literature, especially when dealing with occupancy grids. The Bayesian occupancy grid is the most used method to deal with complex environments. However, this is not fitted to compute the risk along a path by its discrete nature. In this article, we present a new way to store the occupancy of the environment that allows the computation of risk along a given path. We then define the risk as the force of collision that would occur for a given obstacle. Using this framework, we are able to generate navigation paths ensuring the safety of the robot.
BibTeX
@inproceedings{iros2019_lambdafieldacont,
title = {Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment},
author = {Johann Laconte and Christophe Debain and Roland Chapuis and François Pomerleau and Romuald Aufrère},
booktitle = {IROS 2019},
year = {2019}
}