PlaneSDF-based Change Detection for Long-term Dense Mapping
- URL: http://arxiv.org/abs/2207.08323v1
- Date: Mon, 18 Jul 2022 00:19:45 GMT
- Title: PlaneSDF-based Change Detection for Long-term Dense Mapping
- Authors: Jiahui Fu, Chengyuan Lin, Yuichi Taguchi, Andrea Cohen, Yifu Zhang,
Stephen Mylabathula, and John J. Leonard
- Abstract summary: We look into the problem of change detection based on a novel map representation, dubbed Plane Signed Distance Fields (PlaneSDF)
Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach.
We evaluate our approach on both synthetic and real-world datasets and demonstrate its effectiveness via the task of changed object detection.
- Score: 10.159737713094119
- License: http://creativecommons.org/licenses/by/4.0/
- 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 paper, 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.
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