PointLoc：LiDAR点群のための深層ポーズ回帰による自己位置推定
PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
単一のLiDAR点群から地図なしで6自由度ポーズを直接推定するPointNet風の自己注意機構付きエンドツーエンド学習フレームワークを提案し、Oxford Radar RobotCarデータセットで高精度な再位置推定を実現した。
著者: Wei Wang, Bing Wang, Peijun Zhao, Changhao Chen, Ronald Clark, Bo Yang, Andrew Markham, Niki Trigoni
分類: cs.RO
原文アブストラクト
In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric information about a scene. However, LiDAR point clouds are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360{\deg} LiDAR input frames.Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposedmethod can achieve accurate relocalization performance.
