Estimating Uncertain Spatial Relationships in Robotics
This work addresses a foundational challenge in robotics for enabling more accurate and reliable spatial reasoning, representing a novel method rather than an incremental improvement.
The paper tackles the problem of estimating uncertain relative spatial relationships in robotics by introducing the stochastic map representation, which provides probabilistic estimates of object relationships and their uncertainties, advancing beyond previous conservative worst-case approaches.
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.