共有埋め込みを用いたLiDAR地図上でのレーダー自己位置推定の改善
Improved Radar Localization on Lidar Maps Using Shared Embedding
レーダースキャンとLiDAR地図を共通の埋め込み空間に写像するニューラルネットワークを構築し、異種センサ間のギャップを埋めることで、地図検索とデータマッチングによるレーダー自己位置推定・姿勢追跡を高精度化した研究。
著者: Huan Yin, Yue Wang, Rong Xiong
分類: cs.RO, cs.CV
原文アブストラクト
We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps. To bridge the gap of sensing modalities, deep neural networks are constructed to create shared embedding space for radar scans and lidar maps. Herein learned feature embeddings are supportive for similarity measurement, thus improving map retrieval and data matching respectively. In RobotCar and MulRan datasets, we demonstrate the effectiveness of the proposed framework with the comparison to Scan Context and RaLL. In addition, the proposed pose tracking pipeline is with less neural networks compared to the original RaLL.
