LiDAR-Based Place Recognition For Autonomous Driving: A Survey
arXiv:2306.10561 · doi:10.1145/3707446
Abstract
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.
Accepted by ACM Computing Surveys
References in corpus (17)
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
- OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition
- The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
- Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
- MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity
- A fast, complete, point cloud based loop closure for LiDAR odometry and mapping
- RANSAC Back to SOTA: A Two-stage Consensus Filtering for Real-time 3D Registration
- General Place Recognition Survey: Towards the Real-world Autonomy Age
- Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments
- Correcting Motion Distortion for LIDAR HD-Map Localization
- RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map
- A Survey on Visual Map Localization Using LiDARs and Cameras
- PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding
- Delay-aware Robust Control for Safe Autonomous Driving and Racing
- SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
- Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map