BVMatch: Lidar-based Place Recognition Using Bird's-eye View Images

TL;DR

BVMatch is a novel Lidar-based place recognition framework that uses bird's-eye view images and a new descriptor to improve accuracy and robustness in large-scale environments.

Contribution

Introduces BVMatch, a framework combining bird's-eye view images and a rotation-insensitive descriptor for improved Lidar place recognition and pose estimation.

Findings

Outperforms state-of-the-art methods in recall rate

Achieves higher pose estimation accuracy

Effective in large-scale datasets

Abstract

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.