GroundLoc: Efficient Large-Scale Outdoor LiDAR-Only Localization

TL;DR

GroundLoc is a LiDAR-only localization system that efficiently localizes robots in large outdoor environments using 2D raster maps and a BEV image projection, outperforming state-of-the-art methods.

Contribution

GroundLoc introduces a novel LiDAR-only localization pipeline utilizing BEV image projection and flexible keypoint detection, supporting various sensors and achieving high accuracy with minimal storage.

Findings

Outperforms state-of-the-art methods on SemanticKITTI and HeLiPR datasets.

Achieves sub-50cm ATE across multiple sensor types.

Requires only 4 MB per square kilometer of storage for maps.

Abstract

In this letter, we introduce GroundLoc, a LiDAR-only localization pipeline designed to localize a mobile robot in large-scale outdoor environments using prior maps. GroundLoc employs a Bird's-Eye View (BEV) image projection focusing on the perceived ground area and utilizes the place recognition network R2D2, or alternatively, the non-learning approach Scale-Invariant Feature Transform (SIFT), to identify and select keypoints for BEV image map registration. Our results demonstrate that GroundLoc outperforms state-of-the-art methods on the SemanticKITTI and HeLiPR datasets across various sensors. In the multi-session localization evaluation, GroundLoc reaches an Average Trajectory Error (ATE) well below 50 cm on all Ouster OS2 128 sequences while meeting online runtime requirements. The system supports various sensor models, as evidenced by evaluations conducted with Velodyne HDL-64E, Ouster OS2 128, Aeva Aeries II, and Livox Avia sensors. The prior maps are stored as 2D raster image maps, which can be created from a single drive and require only 4 MB of storage per square kilometer. The source code is available at https://github.com/dcmlr/groundloc.