Increasing the Efficiency of 6-DoF Localisation Using Multi-Modal Sensory Data

Ronald Clark, Sen Wang, Hongkai Wen, Niki Trigoni, Andrew Markham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


Localization is a key requirement for mobile robot autonomy and human-robot interaction. Vision-based localization is accurate and flexible, however, it incurs a high computational burden which limits its application on many resource-constrained platforms. In this paper, we address the problem of performing real-time localization in large-scale 3D point cloud maps of ever-growing size. While most systems using multi-modal information reduce localization time by employing side-channel information in a coarse manner (eg. WiFi for a rough prior position estimate), we propose to inter-weave the map with rich sensory data. This multi-modal approach achieves two key goals simultaneously. First, it enables us to harness additional sensory data to localise against a map covering a vast area in real-time; and secondly, it also allows us to roughly localise devices which are not equipped with a camera. The key to our approach is a localization policy based on a sequential Monte Carlo estimator. The localiser uses this policy to attempt point-matching only in nodes where it is likely to succeed, significantly increasing the efficiency of the localization process. The proposed multi-modal localization system is evaluated extensively in a large museum building. The results show that our multi-modal approach not only increases the localization accuracy but significantly reduces computational time.
Original languageEnglish
Title of host publication2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)
Number of pages8
ISBN (Electronic)9781509047185
Publication statusPublished - 2 Jan 2017

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
ISSN (Electronic)2164-0580


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