# Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment

## TL;DR

This paper introduces the Lambda-Field, a continuous occupancy representation that enables accurate risk assessment along paths for autonomous robot navigation, improving safety and path planning.

## Contribution

It proposes the Lambda-Field as a novel continuous occupancy model for better risk computation compared to traditional Bayesian occupancy grids.

## Key findings

- Lambda-Field allows precise risk calculation along robot paths.
- The method improves safety in autonomous navigation.
- Path planning with Lambda-Field ensures robot safety.

## Abstract

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.