# PFilter: Building Persistent Maps through Feature Filtering for Fast and Accurate LiDAR-based SLAM

arXiv: https://arxiv.org/abs/2208.14848 · HTML: https://mlsanity.com/paper/2208.14848/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2022.

## Built on by (most cited first, 7 of 7)

- [Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection](https://mlsanity.com/paper/2306.01438/) (2023)
- [HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation](https://mlsanity.com/paper/2403.11817/) (2024)
- [PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/2310.13295/) (2023)
- [MM-Gaussian: 3D Gaussian-based Multi-modal Fusion for Localization and Reconstruction in Unbounded Scenes](https://mlsanity.com/paper/2404.04026/) (2024)
- [Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures](https://mlsanity.com/paper/2407.11644/) (2024)
- [Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing](https://mlsanity.com/paper/2411.02553/) (2024)
- [RMS: Redundancy-Minimizing Point Cloud Sampling for Real-Time Pose Estimation](https://mlsanity.com/paper/2312.07337/) (2023)

## Abstract (arXiv)

Simultaneous localization and mapping (SLAM) based on laser sensors has been widely adopted by mobile robots and autonomous vehicles. These SLAM systems are required to support accurate localization with limited computational resources. In particular, point cloud registration, i.e., the process of matching and aligning multiple LiDAR scans collected at multiple locations in a global coordinate framework, has been deemed as the bottleneck step in SLAM. In this paper, we propose a feature filtering algorithm, PFilter, that can filter out invalid features and can thus greatly alleviate this bottleneck. Meanwhile, the overall registration accuracy is also improved due to the carefully curated feature points. We integrate PFilter into the well-established scan-to-map LiDAR odometry framework, F-LOAM, and evaluate its performance on the KITTI dataset. The experimental results show that PFilter can remove about 48.4% of the points in the local feature map and reduce feature points in scan by 19.3% on average, which save 20.9% processing time per frame. In the mean time, we improve the accuracy by 9.4%.