PlaneSDF-Based Change Detection for Long-Term Dense Mapping
Name
PlaneSDF-Based_Change_Detection_for_Long-Term_Dense_Mapping.pdf
Size
2.07 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Fu, Jiahui
Lin, Chengyuan
Taguchi, Yuichi
Cohen, Andrea
Zhang, Yifu
Mylabathula, Stephen
Leonard, John J.
Date Issued
October 2022
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
J. Fu et al., "PlaneSDF-Based Change Detection for Long-Term Dense Mapping," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9667-9674, Oct. 2022.
Version
Final published version
Abstract
The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents to detect changes amongst maps of different sessions so as to gain a conflict-free understanding of the current environment. In this letter, we look into the problem of change detection based on a novel map representation, dubbed Plane Signed Distance Fields (PlaneSDF), where dense maps are represented as a collection of planes and their associated geometric components in SDF volumes. Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach: (1) PlaneSDF volumes are instantiated within each scene and registered across scenes using plane poses; 2D height maps and object maps are extracted per volume via height projection and connected component analysis. (2) Height maps are compared and intersected with the object map to produce a 2D change location mask for changed object candidates in the source scene. (3) 3D geometric validation is performed using SDF-derived features per object candidate for change mask refinement. We evaluate our approach on both synthetic and real-world datasets and demonstrate its effectiveness via the task of changed object detection.
Subjects
Artificial Intelligence
Control and Optimization
Computer Science Applications
Computer Vision and Pattern Recognition
Mechanical Engineering
Human-Computer Interaction
Biomedical Engineering
Control and Systems Engineering
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/lra.2022.3191794
