空間解析による複雑環境でのスケーラブルかつ弾力的なLiDAR再構成
Scalable and Elastic LiDAR Reconstruction in Complex Environments Through Spatial Analysis
LiDARによる3D再構成で、空間的な重なりを解析してサブマップを融合・分割し、環境の広さに応じてメモリ使用量を抑えつつ精度を高める手法を提案した論文。
著者: Yiduo Wang, Milad Ramezani, Matias Mattamala, Maurice Fallon
分類: cs.RO
原文アブストラクト
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.
