LiSTA: geometric object-based change detection in cluttered environments

We present LiSTA (LiDAR Spatio-Temporal Analysis), a system to detect probabilistic object-level change over time using multi-mission SLAM. Many applications require such a system, including construction, robotic navigation, long-term autonomy, and environmental monitoring. We focus on the semi-stat...

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Bibliographic Details
Main Authors: Rowell, J, Zhang, L, Fallon, M
Format: Conference item
Language:English
Published: IEEE 2024
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author Rowell, J
Zhang, L
Fallon, M
author_facet Rowell, J
Zhang, L
Fallon, M
author_sort Rowell, J
collection OXFORD
description We present LiSTA (LiDAR Spatio-Temporal Analysis), a system to detect probabilistic object-level change over time using multi-mission SLAM. Many applications require such a system, including construction, robotic navigation, long-term autonomy, and environmental monitoring. We focus on the semi-static scenario where objects are added, subtracted, or changed in position over weeks or months. Our system combines multi-mission LiDAR SLAM, volumetric differencing, object instance description, and correspondence grouping using learned descriptors to keep track of an open set of objects. Object correspondences between missions are determined by clustering the object’s learned descriptors. We demonstrate our approach using datasets collected in a simulated environment and a real-world dataset captured using a LiDAR system mounted on a quadruped robot monitoring an industrial facility containing static, semi-static, and dynamic objects. Our method demonstrates superior performance in detecting changes in semi-static environments compared to existing methods.
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spelling oxford-uuid:2d8c9c74-d6de-4931-b9be-8ffedf790bb02024-10-21T11:36:20ZLiSTA: geometric object-based change detection in cluttered environmentsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:2d8c9c74-d6de-4931-b9be-8ffedf790bb0EnglishSymplectic ElementsIEEE2024Rowell, JZhang, LFallon, MWe present LiSTA (LiDAR Spatio-Temporal Analysis), a system to detect probabilistic object-level change over time using multi-mission SLAM. Many applications require such a system, including construction, robotic navigation, long-term autonomy, and environmental monitoring. We focus on the semi-static scenario where objects are added, subtracted, or changed in position over weeks or months. Our system combines multi-mission LiDAR SLAM, volumetric differencing, object instance description, and correspondence grouping using learned descriptors to keep track of an open set of objects. Object correspondences between missions are determined by clustering the object’s learned descriptors. We demonstrate our approach using datasets collected in a simulated environment and a real-world dataset captured using a LiDAR system mounted on a quadruped robot monitoring an industrial facility containing static, semi-static, and dynamic objects. Our method demonstrates superior performance in detecting changes in semi-static environments compared to existing methods.
spellingShingle Rowell, J
Zhang, L
Fallon, M
LiSTA: geometric object-based change detection in cluttered environments
title LiSTA: geometric object-based change detection in cluttered environments
title_full LiSTA: geometric object-based change detection in cluttered environments
title_fullStr LiSTA: geometric object-based change detection in cluttered environments
title_full_unstemmed LiSTA: geometric object-based change detection in cluttered environments
title_short LiSTA: geometric object-based change detection in cluttered environments
title_sort lista geometric object based change detection in cluttered environments
work_keys_str_mv AT rowellj listageometricobjectbasedchangedetectioninclutteredenvironments
AT zhangl listageometricobjectbasedchangedetectioninclutteredenvironments
AT fallonm listageometricobjectbasedchangedetectioninclutteredenvironments