Scalable and Elastic LiDAR Reconstruction in Complex Environments Through Spatial Analysis
This addresses memory scaling issues in dense 3D reconstruction for robotics and mapping applications, though it appears incremental relative to existing submap-based approaches.
The paper tackles the problem of scalable 3D LiDAR reconstruction in complex environments by developing a system that uses spatial analysis to control memory usage and improve global consistency, demonstrating improved scalability and accuracy in multi-floor indoor, large-scale outdoor, and simulated experiments.
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.