Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2023

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. The key differentiators in our work are: (a) First, unlike traditional localization approaches, our approach is robust to dynamically changing environmental conditions (e.g., varying crowd levels, object placement/layout changes); (b) Second, unlike prior work on visual and 3D SLAM, LiLoc is not dependent on a pre-built static map of the environment and instead works by utilizing dynamically updated point clouds captured from both infrastructural-mounted LiDARs and LiDARs equipped on individual mobile IoT devices. To achieve fine-grained, near real-time location tracking, it employs complex 3D ‘global’ registration among the two point clouds only intermittently to obtain robust spot location estimates and further augments it with repeated simpler ‘local’ registrations to update the trajectory of IoT device continuously. 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.

Keywords

LiDAR, 3D Localization, Pose Estimation, Trajectory Tracking, Dynamic Indoor Environments

Discipline

Data Science | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, San Antonio, USA, 2023 May 9-12

First Page

158

Last Page

171

ISBN

9798400700378

Identifier

10.1145/3576842.3582364

Publisher

Association for Computing Machinery

City or Country

San Antonio, USA

Embargo Period

6-25-2023

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3576842.3582364

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