Bayesian segmentation of laser range scan for indoor navigation

Abstract

This paper presents a robust probabilistic approach based on the Bayesian estimation theory to extract and tracking line segment parameters from successive laser range scans, acquired during the evolution of a mobile robot in an indoor environment. In this methodology, a likelihood function is modelled and associated to the existence of structured objects around the robot, an uncertainty model associated to the telemetric data is derived and used to update the likelihood function. In this way, the distances to the near objects around the robot are estimated and used in the feedback loop of a sensor-based control navigation strategy. Experiments are performed using a mobile robot equiped with a 2D laser scanner device, validating the application of the Bayesian segmentation methodology in the laser-based robot navigation.