Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment
This work addresses a critical safety issue for autonomous robots by providing a more accurate risk assessment method, though it appears incremental as it builds upon existing occupancy grid frameworks.
The paper tackles the problem of poorly defined risk assessment for autonomous robot navigation using discrete Bayesian occupancy grids by introducing a continuous occupancy representation called Lambda-Field, which enables the computation of collision risk as a force along a path and generates safer navigation paths.
In a context of autonomous robots, one of the most important task is to ensure the safety of the robot and its surrounding. Most of the time, the risk of navigation is simply 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, hence giving poor results. In this article, we present a new way to store the occupancy of the environment that allows the computation of risk for 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.