Using Embodied Data for Localisation and Mapping
In Journal of Field Robotics, Wiley Blackwell, volume 31, pages 263-295, Nov/2014.
Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfil the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the
Simultaneous Localisation and Mapping (SLAM) community has largely focused on optical sensors to solve this problem, and the fact that the robot is a physical entity has largely been ignored. In this paper a hierarchical SLAM framework is proposed that takes the
interaction of the robot with the environment into account. A sequential Monte Carlo filter is used to generate local map segments with a combination of visual and embodied data associations. Constraints between segments are used to generate globally consistent maps
with a focus on suitability for navigation tasks. The proposed method is experimentally verified on two different outdoor robots. The results show that the approach is viable and that the rich modelling of the robot with its environment provides a new modality with the
potential for improving existing visual methods and extending the availability of SLAM in domains where visual processing alone is not sufficient.