PlaneSDF-based Change Detection for Long-term Dense Mapping
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. Supplementary video: https://youtu.be/oh-MQPWTwZI
Cite
@article{arxiv.2207.08323,
title = {PlaneSDF-based Change Detection for Long-term Dense Mapping},
author = {Jiahui Fu and Chengyuan Lin and Yuichi Taguchi and Andrea Cohen and Yifu Zhang and Stephen Mylabathula and John J. Leonard},
journal= {arXiv preprint arXiv:2207.08323},
year = {2022}
}
Comments
8 pages, 7 figures, and 1 table. To be published in Robotics and Automation Letters and IROS 2022. Link to supplementary video added in the abstract: https://youtu.be/oh-MQPWTwZI