Online Geometric Change Detection via Scene Decomposition

2026-09-15

AI Summary

本文提出了一种基于场景分解的在线几何变化检测框架CDSD，利用LiDAR或RGB-D传感器解决动态环境中机器人自主识别如倒下的树木或打开的门等环境变化的问题。

Abstract

Autonomous robots are increasingly deployed on long duration single- and multi-session missions in dynamic environments, where the ability to identify environmental changes such as fallen trees or opened doors provides important contextual information for online planning. We propose a framework called Change Detection via Scene Decomposition (CDSD) for accurate online geometric change detection using LiDAR or RGB-D sensors. Recent advances in geometric SLAM have made it possible to generate dense, tightly aligned maps without post processing, but comparing global maps across entire sessions is computationally expensive and does not allow for single-session online change detection. CDSD instead spatially decomposes mapped environments into unique scenes where changes can be found efficiently by comparing dense, local subsets of the global map called submaps. As the first submap-based approach for geometric change detection, we identify and address the following core challenges: 1) identifying appropriate scenes for change detection that require minimal redundant information; 2) generating dense and representative submaps for each scene; 3) detecting changes between submaps with differing fields of view; and 4) processing detected changes for real-time map reconstruction. Results demonstrate our algorithm on custom datasets collected at the Army Research Laboratory facility in Graces Quarters, Maryland, and on open-source multi-session change detection datasets.

Problem

• Autonomous Robots
• Geometric Change Detection
• Dynamic Environments
• Online Planning
• LiDAR

Innovation

• Change Detection via Scene Decomposition (CDSD)
• submaps
• online geometric change detection
• dense maps
• real-time map reconstruction