RKHS-BA: セマンティック点群を用いたロバストな対応点不要の多視点位置合わせフレームワーク
RKHS-BA: A Robust Correspondence-Free Multi-View Registration Framework with Semantic Point Clouds
RGB-D/LiDARとセマンティック情報を再生成カーネルヒルベルト空間で連続的に表現し、対応点を必要としないバンドル調整により多視点点群の位置合わせを高精度かつロバストに行う手法を提案した。
著者: Ray Zhang, Jingwei Song, Xiang Gao, Junzhe Wu, Tianyi Liu, Jinyuan Zhang, Ryan Eustice, Maani Ghaffari
分類: cs.RO
原文アブストラクト
This work reports a novel multi-frame Bundle Adjustment (BA) framework called RKHS-BA. It uses continuous landmark representations that encode RGB-D/LiDAR and semantic observations in a Reproducing Kernel Hilbert Space (RKHS). With a correspondence-free pose graph formulation, the proposed system constructs a loss function that achieves more generalized convergence than classical point-wise convergence. We demonstrate its applications in multi-view point cloud registration, sliding-window odometry, and global LiDAR mapping on simulated and real data. It shows highly robust pose estimations in extremely noisy scenes and exhibits strong generalization with various types of semantic inputs. The open source implementation is released in https://github.com/UMich-CURLY/RKHS_BA.
