# GroundLoc: Efficient Large-Scale Outdoor LiDAR-Only Localization

arXiv: https://arxiv.org/abs/2510.24623 · HTML: https://mlsanity.com/paper/2510.24623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [LO-Net: Deep Real-time Lidar Odometry](https://mlsanity.com/paper/1904.08242/) (2019)
- [F-LOAM: Fast LiDAR Odometry And Mapping](https://mlsanity.com/paper/2107.00822/) (2021)
- [SegMap: Segment-based mapping and localization using data-driven descriptors](https://mlsanity.com/paper/1909.12837/) (2019)
- [PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency](https://mlsanity.com/paper/2401.09101/) (2024)
- [CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure](https://mlsanity.com/paper/2109.12979/) (2021)
- [Learning to Localize Using a LiDAR Intensity Map](https://mlsanity.com/paper/2012.10902/) (2020)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatiotemporal Variations](https://mlsanity.com/paper/2309.14590/) (2023)
- [Range Image-based LiDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2105.12121/) (2021)
- [Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes](https://mlsanity.com/paper/1711.05805/) (2017)
- [A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments](https://mlsanity.com/paper/2203.06612/) (2022)
- [Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry](https://mlsanity.com/paper/2109.07082/) (2021)
- [GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation](https://mlsanity.com/paper/2405.15664/) (2024)
- [LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps](https://mlsanity.com/paper/2007.01595/) (2020)
- [Learning to Localize Through Compressed Binary Maps](https://mlsanity.com/paper/2012.10942/) (2020)
- [KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities](https://mlsanity.com/paper/2503.12660/) (2025)

## Abstract (arXiv)

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.