Simultaneous parameter calibration, localization, and mapping for robust service robotics

Modern service robots are designed to be deployed by end-users and not to be monitored by experts during operation. Most service robotics applications require reliable navigation capabilities of the robot. The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on environmental changes or on the wear of the devices. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the onboard sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the platform parameters. The proposed approach performs on-line estimation of the parameters and it is able to adapt to non-stationary changes of the configuration. Our approach has been implemented and is used on the EUROPA robot, a service robot operating in urban environments. In addition to that, we tested our approach in simulated environments and on a wide range of real world data using different types of robotic platforms.