Outram: 三角測量シーングラフと大域的外れ値除去によるワンショット全局位置推定
Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning
3Dシーングラフの部分構造を活用し、局所的な対応探索と大域的な外れ値除去を階層的に組み合わせることで、単一のLiDAR点群からロボットの姿勢を高精度に推定する手法を提案した。
著者: Pengyu Yin, Haozhi Cao, Thien-Minh Nguyen, Shenghai Yuan, Shuyang Zhang, Kangcheng Liu, Lihua Xie
分類: cs.RO
原文アブストラクト
One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively studied as a global descriptor retrieval (i.e., loop closure detection) and pose refinement (i.e., point cloud registration) problem both in isolation or combined. However, few have explicitly considered the relationship between candidate retrieval and correspondence generation in pose estimation, leaving them brittle to substructure ambiguities. To this end, we propose a hierarchical one-shot localization algorithm called Outram that leverages substructures of 3D scene graphs for locally consistent correspondence searching and global substructure-wise outlier pruning. Such a hierarchical process couples the feature retrieval and the correspondence extraction to resolve the substructure ambiguities by conducting a local-to-global consistency refinement. We demonstrate the capability of Outram in a variety of scenarios in multiple large-scale outdoor datasets. Our implementation is open-sourced: https://github.com/Pamphlett/Outram.
