LILOC: Leveraging LiDARs for Accurate 3D Localization in Dynamic Indoor Environments

Abstract

We present LiLoc , a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. LiLoc stands out with two key differentiators: Firstly, unlike traditional localization approaches, our method remains robust in dynamically changing environments, adeptly handling varying crowd levels and object layout changes. Secondly, LiLoc is independent of pre-built static maps, employing dynamically updated point clouds from infrastructural-mounted LiDARs and LiDARs on individual IoT devices. For fine-grained, near real-time tracking, LiLoc intermittently utilizes complex 3D ”global” registration between point clouds for robust spot location estimates. It further complements this with simpler ”local” registrations, continuously updating IoT device trajectories. We demonstrate that LiLoc can (a) support accurate location tracking with location and pose estimation error being <=7.4cm and <=3.2° respectively for 84% of the time and the median error increasing only marginally (8%), for correctly estimated trajectories, when the ambient environment is dynamic, (b) achieve a 36% reduction in median location estimation error compared to an approach that uses only quasi-static global point cloud, and (c) obtain spot location estimates with a latency of only 973 msecs. We also demonstrate how LiLoc efficiently integrates low-power inertial sensing, using a novel integration of inertial-based displacement to accelerate the local registration process, to enhance localization energy efficiency and latency.