Estimating Uncertain Spatial Relationships in Robotics

TL;DR

This paper introduces a probabilistic representation called the stochastic map for modeling uncertain spatial relationships in robotics, enabling incremental updates and more accurate spatial reasoning.

Contribution

It presents a novel probabilistic framework for representing and updating uncertain spatial relationships, advancing beyond conservative worst-case methods.

Findings

Provides a general solution for estimating uncertain spatial relationships.

Uses probabilistic estimates for improved accuracy.

Develops procedures within state-estimation and filtering theory.

Abstract

In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map contains the estimates of relationships among objects in the map, and their uncertainties, given all the available information. The procedures provide a general solution to the problem of estimating uncertain relative spatial relationships. The estimates are probabilistic in nature, an advance over the previous, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering theory, which provides a solid basis for numerous extensions.