Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
This work addresses the computational burden of vision-based localization for mobile robots and human-robot interaction, particularly on resource-constrained platforms, though it appears incremental as it builds on existing multi-modal methods with a novel policy.
The paper tackles the problem of real-time 6-DoF visual localization in large-scale 3D point cloud maps, which is computationally intensive, by proposing a multi-modal approach that inter-weaves sensory data into the map and uses a sequential Monte Carlo estimator to increase efficiency. The results show that this method increases localization accuracy and significantly reduces computational time in evaluations conducted in a large museum building.
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.