LILOC: Enabling Precise 3D Localization in Dynamic Indoor Environments using LiDARs

Rathnayake, D; Radhakrishnan, M; Hwang, I; Misra, A

LILOC: Enabling Precise 3D Localization in Dynamic Indoor Environments using LiDARs

Rathnayake, D Radhakrishnan, M

Hwang, I Misra, A

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2023, pp. 158-171
Issue Date:: 2023-05-09

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Field	Value	Language
dc.contributor.author	Rathnayake, D
dc.contributor.author	Radhakrishnan, M https://orcid.org/0000-0002-9884-1769
dc.contributor.author	Hwang, I
dc.contributor.author	Misra, A
dc.date.accessioned	2024-01-11T05:17:15Z
dc.date.available	2024-01-11T05:17:15Z
dc.date.issued	2023-05-09
dc.identifier.citation	ACM International Conference Proceeding Series, 2023, pp. 158-171
dc.identifier.isbn	9798400700378
dc.identifier.uri	http://hdl.handle.net/10453/174318
dc.description.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 ĝglobal' registration among the two point clouds only intermittently to obtain robust spot location estimates and further augments it with repeated simpler ĝlocal' 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.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM International Conference Proceeding Series
dc.relation.ispartof	Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
dc.relation.isbasedon	10.1145/3576842.3582364
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	LILOC: Enabling Precise 3D Localization in Dynamic Indoor Environments using LiDARs
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-11T05:17:09Z
pubs.publication-status	Published

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 ĝglobal' registration among the two point clouds only intermittently to obtain robust spot location estimates and further augments it with repeated simpler ĝlocal' 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.

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http://hdl.handle.net/10453/174318