Inertial Systems Based Joint Mapping and Positioning for Pedestrian Navigation

We present results for a new pedestrian localisation technique that builds on the principle of Simultaneous Localization and Mapping (SLAM). Our approach is called FootSLAM since it is based mainly on the use of shoe-mounted inertial sensors that are used to measure a pedestrian’s steps while walking. In contrast to SLAM used in robotics no specific feature-detection sensors such as cameras or laser scanners are needed in our approach. The work extends prior work in pedestrian navigation that uses known building plan layouts to constrain a location estimation algorithm driven by a stride estimation process. In our approach building plans (maps) can be learnt automatically while people walk in a building. This can be done either directly to localise this specific person or in a offline fashion in order to provide maps for other people. We have combined our system with a GPS and have undertaken experiments in the important scenario where a person enters a building from outside and walks around within this building without GPS availability. Our experiments were undertaken by recording the raw sensor data and ground truth reference information. Offline procesing and comparison with the ground truth reference information allows quantitative evaluation of the achieved localisation accuracy.