3D LiDAR自己位置推定のための重複度ベース観測モデルの学習
Learning an Overlap-based Observation Model for 3D LiDAR Localization
3D LiDARスキャン間の重複度とヨー角オフセットを予測するニューラルネット観測モデルを提案し、モンテカルロ自己位置推定に統合して都市環境での車両位置推定を高精度化した論文。
著者: Xieyuanli Chen, Thomas Läbe, Lorenzo Nardi, Jens Behley, Cyrill Stachniss
分類: cs.RO
原文アブストラクト
Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data. We propose a novel, neural network-based observation model that computes the expected overlap of two 3D LiDAR scans. The model predicts the overlap and yaw angle offset between the current sensor reading and virtual frames generated from a pre-built map. We integrate this observation model into a Monte-Carlo localization framework and tested it on urban datasets collected with a car in different seasons. The experiments presented in this paper illustrate that our method can reliably localize a vehicle in typical urban environments. We furthermore provide comparisons to a beam-end point and a histogram-based method indicating a superior global localization performance of our method with fewer particles.
