# LiDAR-Based Place Recognition For Autonomous Driving: A Survey

arXiv: https://arxiv.org/abs/2306.10561 · HTML: https://mlsanity.com/paper/2306.10561/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2023.

## Built on by (most cited first, 21 of 21)

- [RANSAC Back to SOTA: A Two-stage Consensus Filtering for Real-time 3D Registration](https://mlsanity.com/paper/2410.15682/) (2024)
- [SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation](https://mlsanity.com/paper/2502.20077/) (2025)
- [Ensemble-Based Event Camera Place Recognition Under Varying Illumination](https://mlsanity.com/paper/2509.01968/) (2025)
- [CaLDiff: Camera Localization in NeRF via Pose Diffusion](https://mlsanity.com/paper/2312.15242/) (2023)
- [PoCo: Point Context Cluster for RGBD Indoor Place Recognition](https://mlsanity.com/paper/2404.02885/) (2024)
- [Vehicle-Scene Interaction: A Text-Driven 3D Lidar Place Recognition Method for Autonomous Driving](https://mlsanity.com/paper/2503.18035/) (2025)
- [Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization](https://mlsanity.com/paper/2504.07677/) (2025)
- [Ranking-aware Continual Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2505.07198/) (2025)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)
- [PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition](https://mlsanity.com/paper/2603.05965/) (2026)
- [Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization](https://mlsanity.com/paper/2606.10019/) (2026)
- [Fast 3D point clouds retrieval for Large-scale 3D Place Recognition](https://mlsanity.com/paper/2502.21067/) (2025)
- [Diffusion Based Robust LiDAR Place Recognition](https://mlsanity.com/paper/2504.12412/) (2025)
- [TopoRec: Point Cloud Recognition Using Topological Data Analysis](https://mlsanity.com/paper/2506.18725/) (2025)
- [S-BEVLoc: BEV-based Self-supervised Framework for Large-scale LiDAR Global Localization](https://mlsanity.com/paper/2509.09110/) (2025)
- [Learning from Mistakes: Loss-Aware Memory Enhanced Continual Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2511.15597/) (2025)
- [WHU-PCPR: A cross-platform heterogeneous point cloud dataset for place recognition in complex urban scenes](https://mlsanity.com/paper/2601.06442/) (2026)
- [From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization](https://mlsanity.com/paper/2602.03198/) (2026)
- [Voronoi-based Second-order Descriptor with Whitened Metric in LiDAR Place Recognition](https://mlsanity.com/paper/2603.14974/) (2026)
- [EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition](https://mlsanity.com/paper/2605.02275/) (2026)
- [Temporal-Aware Fusion for Robust Outdoor LiDAR Localization](https://mlsanity.com/paper/2609.36432/) (2026)

## Abstract (arXiv)

LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated errors and achieving reliable localization. However, existing reviews predominantly concentrate on visual place recognition (VPR) methods. Despite the recent remarkable progress in LPR, to the best of our knowledge, there is no dedicated systematic review in this area. This paper bridges the gap by providing a comprehensive review of place recognition methods employing LiDAR sensors, thus facilitating and encouraging further research. We commence by delving into the problem formulation of place recognition, exploring existing challenges, and describing relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets, commonly used evaluation metrics, and comprehensive evaluation results from various methods on public datasets. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition and for researchers interested in long-term robot localization. We pledge to maintain an up-to-date project on our website https://github.com/ShiPC-AI/LPR-Survey.