Radar-on-Lidar: 事前LiDAR地図上でのメートル精度レーダ自己位置推定
Radar-on-Lidar: metric radar localization on prior lidar maps
回転レーダの生データをGANでLiDARデータに変換し、レーダオドメトリと組み合わせたモンテカルロ法により、事前の2D LiDAR地図上で高精度に自己位置推定する手法を提案した。
著者: Huan Yin, Yue Wang, Li Tang, Rong Xiong
分類: cs.RO
原文アブストラクト
Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes.
