Estimating Uncertain Spatial Relationships in Robotics

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.

The research reported in this paper was supported by the National Science Foundation under Grant ECS- 8200615, the Air Force Office of Scientific Research under Contract F49620-84-K-0007, and by General Motors Research Laboratories.