BVMatch: Lidar-Based Place Recognition Using Bird's-Eye View Images
نویسندگان
چکیده
Recognizing places using Lidar in large-scale environments is challenging due to the sparse nature of point cloud data. In this letter we present BVMatch, a Lidar-based frame-to-frame place recognition framework, that capable estimating 2D relative poses. Based on assumption ground area can be approximated as plane, uniformly discretize into grids and project 3D scans bird's-eye view (BV) images. We further use bank Log-Gabor filters build maximum index map (MIM) encodes orientation information structures analyze characteristics MIM theoretically introduce novel descriptor called feature transform (BVFT). The proposed BVFT insensitive rotation intensity variations BV Leveraging descriptors, unify pose estimation tasks BVMatch framework. experiments conducted three datasets show outperforms state-of-the-art methods terms both recall rate accuracy.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2021
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2021.3091386