# F-LOAM: Fast LiDAR Odometry And Mapping

arXiv: https://arxiv.org/abs/2107.00822 · HTML: https://mlsanity.com/paper/2107.00822/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2021.

## Built on by (most cited first, 30 of 45)

- [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)
- [HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2203.09215/) (2022)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations](https://mlsanity.com/paper/2212.11770/) (2022)
- [DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment](https://mlsanity.com/paper/2403.18274/) (2024)
- [HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion](https://mlsanity.com/paper/2304.00932/) (2023)
- [Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2202.12197/) (2022)
- [VBR: A Vision Benchmark in Rome](https://mlsanity.com/paper/2404.11322/) (2024)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM](https://mlsanity.com/paper/2309.08086/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes](https://mlsanity.com/paper/2505.18992/) (2025)
- [PG-RCNN: Semantic Surface Point Generation for 3D Object Detection](https://mlsanity.com/paper/2307.12637/) (2023)
- [PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map](https://mlsanity.com/paper/2502.05752/) (2025)
- [Quadric Representations for LiDAR Odometry, Mapping and Localization](https://mlsanity.com/paper/2304.14190/) (2023)
- [Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory](https://mlsanity.com/paper/2309.13842/) (2023)
- [HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2409.04398/) (2024)
- [KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping](https://mlsanity.com/paper/2309.15394/) (2023)
- [MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry](https://mlsanity.com/paper/2405.05828/) (2024)
- [SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset](https://mlsanity.com/paper/2410.21739/) (2024)
- [SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios](https://mlsanity.com/paper/2301.07279/) (2023)
- [SAGE-ICP: Semantic Information-Assisted ICP](https://mlsanity.com/paper/2310.07237/) (2023)
- [Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2211.08754/) (2022)
- [Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration](https://mlsanity.com/paper/2410.05729/) (2024)
- [LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks](https://mlsanity.com/paper/2501.19382/) (2025)
- [Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency Transfer](https://mlsanity.com/paper/2308.14332/) (2023)
- [Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures](https://mlsanity.com/paper/2407.11644/) (2024)
- [PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection](https://mlsanity.com/paper/2408.14600/) (2024)
- [ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle](https://mlsanity.com/paper/2501.02166/) (2025)

## Abstract (arXiv)

Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as autonomous driving and unmanned aerial vehicles. Both computational efficiency and localization accuracy are of great importance towards a good SLAM system. Existing works on LiDAR based SLAM often formulate the problem as two modules: scan-to-scan match and scan-to-map refinement. Both modules are solved by iterative calculation which are computationally expensive. In this paper, we propose a general solution that aims to provide a computationally efficient and accurate framework for LiDAR based SLAM. Specifically, we adopt a non-iterative two-stage distortion compensation method to reduce the computational cost. For each scan input, the edge and planar features are extracted and matched to a local edge map and a local plane map separately, where the local smoothness is also considered for iterative pose optimization. Thorough experiments are performed to evaluate its performance in challenging scenarios, including localization for a warehouse Automated Guided Vehicle (AGV) and a public dataset on autonomous driving. The proposed method achieves a competitive localization accuracy with a processing rate of more than 10 Hz in the public dataset evaluation, which provides a good trade-off between performance and computational cost for practical applications.