Bayesian Learning of Occupancy Grids

TL;DR

This paper introduces a Bayesian framework for occupancy grid mapping that accounts for dependencies between cells and improves accuracy by reducing false alarms and detection misses with fewer observations.

Contribution

It presents a novel Bayesian approach that relaxes the independence assumption in occupancy grids using binary asymmetric channel models, enhancing mapping accuracy.

Findings

Lower false alarm rates compared to classical methods

Fewer observations needed for accurate mapping

Improved detection of occupied cells

Abstract

Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells. The problem is to recursively update the probability that each cell in the grid is occupied, based on a sequence of sensor measurements from a moving platform. In this paper, we provide a new Bayesian framework for generating these probabilities that does not assume statistical independence between the occupancy state of grid cells. This approach is made analytically tractable through the use of binary asymmetric channel models that capture the errors associated with observing the occupancy state of a grid cell. Binary-valued measurement vectors are the thresholded output of a sensor in a radar, sonar, or other sensory system. We compare the performance of the proposed framework to that of the classical formulation for occupancy grids. The results show that the proposed framework identifies occupancy grids with lower false alarm and miss detection rates, and requires fewer observations of the surrounding area, to generate an accurate estimate of occupancy probabilities when compared to conventional formulations.