Improved Radar Localization on Lidar Maps Using Shared Embedding

• URL: http://arxiv.org/abs/2106.10000v1
• Date: Fri, 18 Jun 2021 08:40:04 GMT
• Title: Improved Radar Localization on Lidar Maps Using Shared Embedding
• Authors: Huan Yin, Yue Wang and Rong Xiong
• Abstract summary: We present a framework for solving radar global localization and pose tracking on pre-built lidar maps. Deep neural networks are constructed to create shared embedding space for radar scans and lidar maps.
• Score: 12.65429845365685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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