Online parameter estimation of a robot’s motion model

Abstract

Simultaneous localization and mapping (SLAM) algorithms rely heavily on a good motion model to provide critical information about the robot's current pose. Most of these algorithms assume that the distribution defining a robot's motion will remain stationary over the period of operation, and as such use a fixed model for the duration of a trial. This does not easily allow for changes in the robot's motion model due to surface changes, wear and tear, and battery life. Also, if new robots of a similar class are to be used, a new motion model may need to be constructed from scratch. In this paper, we introduce a method that allows the robot to automatically learn its motion model, given a rough estimate of its model or the model from a robot of similar class. We validate our method by demonstrating that it learns a new motion model when a robot crosses a threshold onto a different surface. We also demonstrate our method can estimate the motion model for a new robot given the motion model of a robot of similar class.