  Recognizing places using Lidar in large-scale environments is challenging due
to the sparse nature of point cloud data. In this paper we present BVMatch, a
Lidar-based frame-to-frame place recognition framework, that is capable of
estimating 2D relative poses. Based on the assumption that the ground area can
be approximated as a plane, we uniformly discretize the ground area into grids
and project 3D Lidar scans to bird's-eye view (BV) images. We further use a
bank of Log-Gabor filters to build a maximum index map (MIM) that encodes the
orientation information of the structures in the images. We analyze the
orientation characteristics of MIM theoretically and introduce a novel
descriptor called bird's-eye view feature transform (BVFT). The proposed BVFT
is insensitive to rotation and intensity variations of BV images. Leveraging
the BVFT descriptors, we unify the Lidar place recognition and pose estimation
tasks into the BVMatch framework. The experiments conducted on three
large-scale datasets show that BVMatch outperforms the state-of-the-art methods
in terms of both recall rate of place recognition and pose estimation accuracy.
The source code of our method is publicly available at
https://github.com/zjuluolun/BVMatch.
