Scalable and elastic LiDAR reconstruction in complex environments through spatial analysis

Conference item

This paper presents novel strategies for spawning and fusing submaps within an elastic dense 3D reconstruction system. The proposed system uses spatial understanding of the scanned environment to control memory usage growth by fusing overlapping submaps in different ways. This allows the number of submaps and memory consumption to scale with the size of the environment rather than the duration of exploration. By analysing spatial overlap, our system segments distinct spaces, such as rooms and stairwells on the fly during exploration. Additionally, we present a new mathematical formulation of relative uncertainty between poses to improve the global consistency of the reconstruction. Performance is demonstrated using a multi-floor multi-room indoor experiment, a large-scale outdoor experiment and simulated datasets. Relative to our baseline, the presented approach demonstrates improved scalability and accuracy.

Authors: Wang, Y; Ramezani, M; Mattamala, M; Fallon, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2021 European Conference on Mobile Robots, ECMR 2021 - Proceedings
• Pages: 1-8
• Publication date: 2021-10-21
• Acceptance date: 2021-07-15
• Event title: 10th European Conference on Mobile Robots, ECMR 2021
• Event location: Bonn, Germany
• Event website: https://ecmr2021.org/
• Event start date: 2021-08-31
• Event end date: 2021-09-03
• DOI: 10.1109/ecmr50962.2021.9568844
• Language: English
• Keywords: aerospace electronics; Europe; uncertainty; three-dimensional displays; laser radar; scalability; memory management