Dual occupancy and knowledge maps management for optimal traversability risk analysis

In a context of autonomous driving, perception of the surrounding is a crucial task. It characterizes the vehicle’s ability to simultaneously model its surroundings accurately and maintain its position in the environment. In this article, a new framework of mobile robot perception and risk assessment is proposed. Our approach aims to leverage the simultaneous combination of the standard occupancy grid map with a new map that we have called "knowledge map". This proposal was motivated by the fact that risk arises not only from obstacles but also from the lack of knowledge. Using this framework, we are able to assess the risk, mainly of collision, over a given path $\mathscr{P}$ and therefore compute an optimal navigation control of the robot. Thanks to the proposed Bayesian framework the paper also shows how we can combine both local measurements and existing map (eg. OpenStreetMap) and also take account of the robot’s localization errors.