Simultaneous Localization, Mapping and Self-body Shape Estimation by a Mobile Robot

This paper describes a new method for estimating the body shape of a mobile robot by using sensory-motor information. In many biological systems, it is important to be able to estimate body shapes to allow it to appropriately behave in a complex environment. Humans and other animals can form their body image and determine actions based on their recognized body shape. However, conventional mobile robots have not had the ability to estimate body shape, and instead, developers have provided body shape information to the robots. In this paper, we describe a new method that enables a robot to obtain only subjective information, e.g., motor commands and distance sensor information, and automatically estimate its self-body shape. We call the method simultaneous localization, mapping, and self-body shape estimation (SLAM-SBE). The method is based on Bayesian statistics. In particular, the method is obtained by extending the simultaneous localization and mapping (SLAM) method. Experimental results show that a mobile robot can obtain a self-body shape image represented by an occupancy grid by using only its sensory-motor information (i.e., without any objective measurement of its body).