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

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

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

Rathnayake, D Radhakrishnan, M

Hwang, I Misra, A

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Internet of Things, 2024, 5, (4), pp. 1-33
Issue Date:: 2024-11-30

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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	2024-11-15T23:34:14Z
dc.date.available	2024-11-15T23:34:14Z
dc.date.issued	2024-11-30
dc.identifier.citation	ACM Transactions on Internet of Things, 2024, 5, (4), pp. 1-33
dc.identifier.issn	2577-6207
dc.identifier.issn	2577-6207
dc.identifier.uri	http://hdl.handle.net/10453/181928
dc.description.abstract	<jats:p> We present <jats:italic>LiLoc</jats:italic> , a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. <jats:italic>LiLoc</jats:italic> stands out with two key differentiators. First, unlike traditional localization approaches, our method remains robust in dynamically changing environments, adeptly handling varying crowd levels and object layout changes. Second, <jats:italic>LiLoc</jats:italic> 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, <jats:italic>LiLoc</jats:italic> 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 <jats:italic>LiLoc</jats:italic> can (a) support accurate location tracking with location and pose estimation error being ≦7.4 cm 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 msec. We also demonstrate how <jats:italic>LiLoc</jats:italic> 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. </jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Internet of Things
dc.relation.isbasedon	10.1145/3695881
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	©ACM2024. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 24 October 2024 https://doi.acm.org/10.1145/3695881
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4606 Distributed computing and systems software
dc.title	LILOC: Leveraging LiDARs for Accurate 3D Localization in Dynamic Indoor Environments
dc.type	Journal Article
utslib.citation.volume	5
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2024-11-15T23:34:10Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	5
utslib.citation.issue	4

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. First, unlike traditional localization approaches, our method remains robust in dynamically changing environments, adeptly handling varying crowd levels and object layout changes. Second, 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 ≦7.4 cm 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 msec. 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.

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http://hdl.handle.net/10453/181928