Robot maps broken down into scenes to find changes fast
Online Geometric Change Detection via Scene Decomposition
Robotics
Summary
Finding changes in environments helps robots understand what’s around them and plan better as things change. The authors present a way to break big robot maps into smaller scenes, called submaps, to quickly spot differences like doors opening or trees falling. This method uses detailed 3D data from sensors and works while the robot is moving, instead of waiting until after a long mapping session. It’s tested on real and open datasets, showing it can detect changes efficiently and update the map in real time.
What this means in practice
- •For autonomous robotics teams: Detect environmental changes like opened doors or obstacles during long missions using sensor data segmented into scenes for efficient real-time updates.
- •For security and monitoring teams: Use scene-based change detection with 3D sensors to monitor large indoor or outdoor facilities for unusual structural changes without heavy computing resources.
Authors
David Thorne, Samuel Jia Cong Chua, Nakul Joshi, Aiden Wong, Christa S. Robison, Philip Osteen, Brett T. Lopez
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.