This paper studies indoor localisation problem by using low-cost and pervasive sensors. Most of existing indoor localisation algorithms rely on camera, laser scanner, floor plan or other pre-installed infrastructure to achieve sub-meter or sub-centimetre localisation accuracy. However, in some circumstances these required devices or information may be unavailable or too expensive in terms of cost or deployment. This paper presents a novel keyframe based Pose Graph Simultaneous Localisation and Mapping (SLAM) method, which correlates ambient geomagnetic field with motion pattern and employs low-cost sensors commonly equipped in mobile devices, to provide positioning in both unknown and known environments. Extensive experiments are conducted in large-scale indoor environments to verify that the proposed method can achieve high localisation accuracy similar to state-of-the-arts, such as vision based Google Project Tango.
|Title of host publication||2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|
|Number of pages||8|
|Publication status||Published - 1 Dec 2016|
|Name||Proceedings of the International Conference on Intelligent Robots and Systems|
Wang, S., Wen, H., Clark, R., & Trigoni, N. (2016). Keyframe based Large-Scale Indoor Localisation using Geomagnetic Field and Motion Pattern. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1910-1917). ( Proceedings of the International Conference on Intelligent Robots and Systems). IEEE. https://doi.org/10.1109/IROS.2016.7759302