Self Localisation using Embodied Data for a Hybrid Leg-Wheel Robot

Abstract :

Robotic systems that are able to navigate autonomously in unstructured outdoor terrain have a large potential in a number of applications like planetary exploration or search and rescue scenarios. Localisation is usually performed through dead-reckoning with the help of visual means. The approach described in this paper uses only sensory information internal to the system to localize in a partially known environment. A model of the robot and sensory information on its configuration and orientation are used to match candidate contact points with an environment model. A particle filter implementation is developed, which uses this information together with the odometry to track the pose of the robot. The experiments conducted on a hybrid leg-wheel robot show that the approach is able to track the position of the robot within an average error of 0.5m for test runs of up to 140m distance travelled. One potential of this approach is to reduce the requirements on the visual parts when integrated into SLAM frameworks.