Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception

TL;DR

This paper introduces Occupancy Grids, a probabilistic spatial representation for robot perception that enables robust mapping, navigation, and sensor data integration under uncertainty, demonstrated through various robotic tasks.

Contribution

It presents a stochastic formulation of Occupancy Grids and demonstrates their application to mapping, sensor fusion, and path planning in robotics, highlighting their robustness and versatility.

Findings

Generates dense, probabilistic world models.

Robust under sensor uncertainty and errors.

Supports operations similar to image processing.

Abstract

In this paper we provide an overview of a new framework for robot perception, real-world modelling, and navigation that uses a stochastic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field model that maintains probabilistic estimates of the occupancy state of each cell in a spatial lattice. Bayesian estimation mechanisms employing stochastic sensor models allow incremental updating of the Occupancy Grid using multi-view, multi-sensor data, composition of multiple maps, decision-making, and incorporation of robot and sensor position uncertainty. We present the underlying stochastic formulation of the Occupancy Grid framework, and discuss its application to a variety of robotic tusks. These include range-based mapping, multi-sensor integration, path-planning and obstacle avoidance, handling of robot position uncertainty, incorporation of pre-compiled maps, recovery of geometric representations, and other related problems. The experimental results show that the Occupancy Grid approach generates dense world models, is robust under sensor uncertainty and errors, and allows explicit handling of uncertainty. It supports the development of robust and agile sensor interpretation methods, incremental discovery procedures, and composition of information from multiple sources. Furthermore, the results illustrate that robotic tasks can be addressed through operations performed di- rectly on the Occupancy Grid, and that these operations have strong parallels to operations performed in the image processing domain.