# CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map

arXiv: https://arxiv.org/abs/2110.10194 · HTML: https://mlsanity.com/paper/2110.10194/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 82 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 30 of 31)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds](https://mlsanity.com/paper/1904.00229/) (2019)
- [LO-Net: Deep Real-time Lidar Odometry](https://mlsanity.com/paper/1904.08242/) (2019)
- [Visual Odometry Revisited: What Should Be Learnt?](https://mlsanity.com/paper/1909.09803/) (2019)
- [LDSO: Direct Sparse Odometry with Loop Closure](https://mlsanity.com/paper/1808.01111/) (2018)
- [Hybrid Scene Compression for Visual Localization](https://mlsanity.com/paper/1807.07512/) (2018)
- [Fast Resampling of 3D Point Clouds via Graphs](https://mlsanity.com/paper/1702.06397/) (2017)
- [LIMO: Lidar-Monocular Visual Odometry](https://mlsanity.com/paper/1807.07524/) (2018)
- [LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation](https://mlsanity.com/paper/2009.00164/) (2020)
- [CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction](https://mlsanity.com/paper/1810.01011/) (2018)
- [UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes](https://mlsanity.com/paper/1912.09513/) (2019)
- [LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps](https://mlsanity.com/paper/2007.01595/) (2020)
- [CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR](https://mlsanity.com/paper/1712.06352/) (2017)
- [Correcting rural building annotations in OpenStreetMap using convolutional neural networks](https://mlsanity.com/paper/1901.08190/) (2019)
- [Learning to Localize Through Compressed Binary Maps](https://mlsanity.com/paper/2012.10942/) (2020)
- [Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans](https://mlsanity.com/paper/1910.10550/) (2019)
- [RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model](https://mlsanity.com/paper/2009.07061/) (2020)
- [Learning an Overlap-based Observation Model for 3D LiDAR Localization](https://mlsanity.com/paper/2105.11717/) (2021)
- [From Pixels to Buildings: End-to-end Probabilistic Deep Networks for Large-scale Semantic Mapping](https://mlsanity.com/paper/1812.11866/) (2018)
- [ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA](https://mlsanity.com/paper/1808.03506/) (2018)
- [Deep Direct Visual Odometry](https://mlsanity.com/paper/1912.05101/) (2019)
- [Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA](https://mlsanity.com/paper/1711.02757/) (2017)
- [Radar-on-Lidar: metric radar localization on prior lidar maps](https://mlsanity.com/paper/2005.04644/) (2020)
- [An Interactive LiDAR to Camera Calibration](https://mlsanity.com/paper/1903.02122/) (2019)

## Abstract (arXiv)

LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise and efficient. Due to its non-convexity and its local iterative strategy, however, ICP-based method easily falls into local optima, which in turn calls for a precise initialization. In this paper, we propose CoFi, a Coarse-to-Fine ICP algorithm for LiDAR localization. Specifically, the proposed algorithm down-samples the input point sets under multiple voxel resolution, and gradually refines the transformation from the coarse point sets to the fine-grained point sets. In addition, we propose a map based LiDAR localization algorithm that extracts semantic feature points from the LiDAR frames and apply CoFi to estimate the pose on an efficient point cloud map. With the help of the Cylinder3D algorithm for LiDAR scan semantic segmentation, the proposed CoFi localization algorithm demonstrates the state-of-the-art performance on the KITTI odometry benchmark, with significant improvement over the literature.