# IMLS-SLAM: scan-to-model matching based on 3D data

arXiv: https://arxiv.org/abs/1802.08633 · HTML: https://mlsanity.com/paper/1802.08633/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

## Built on by (most cited first, 30 of 40)

- [Robust Point Cloud Registration Framework Based on Deep Graph Matching](https://mlsanity.com/paper/2103.04256/) (2021)
- [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)
- [Robust Outlier Rejection for 3D Registration with Variational Bayes](https://mlsanity.com/paper/2304.01514/) (2023)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping](https://mlsanity.com/paper/2111.11348/) (2021)
- [Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2109.06619/) (2021)
- [Reliable Inlier Evaluation for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2202.11292/) (2022)
- [SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure](https://mlsanity.com/paper/2106.11516/) (2021)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Quadric Representations for LiDAR Odometry, Mapping and Localization](https://mlsanity.com/paper/2304.14190/) (2023)
- [VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registration](https://mlsanity.com/paper/2203.13241/) (2022)
- [3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation](https://mlsanity.com/paper/2405.03388/) (2024)
- [Efficient LiDAR Odometry for Autonomous Driving](https://mlsanity.com/paper/2104.10879/) (2021)
- [Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2108.02613/) (2021)
- [Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction](https://mlsanity.com/paper/2112.09385/) (2021)
- [End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration](https://mlsanity.com/paper/2110.15250/) (2021)
- [Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory](https://mlsanity.com/paper/2309.13842/) (2023)
- [What's in My LiDAR Odometry Toolbox?](https://mlsanity.com/paper/2103.09708/) (2021)
- [Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds](https://mlsanity.com/paper/2210.14899/) (2022)
- [A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation](https://mlsanity.com/paper/2302.14511/) (2023)
- [Deep Bayesian ICP Covariance Estimation](https://mlsanity.com/paper/2202.11607/) (2022)
- [ECTLO: Effective Continuous-time Odometry Using Range Image for LiDAR with Small FoV](https://mlsanity.com/paper/2206.08517/) (2022)
- [Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents](https://mlsanity.com/paper/2307.07763/) (2023)
- [Overlap Bias Matching is Necessary for Point Cloud Registration](https://mlsanity.com/paper/2308.09364/) (2023)
- [DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors](https://mlsanity.com/paper/2312.02684/) (2023)
- [Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function](https://mlsanity.com/paper/2202.13855/) (2022)
- [Unsigned Distance Field as an Accurate 3D Scene Representation for Neural Scene Completion](https://mlsanity.com/paper/2203.09167/) (2022)
- [Searching Dense Point Correspondences via Permutation Matrix Learning](https://mlsanity.com/paper/2210.14897/) (2022)
- [A Strong Baseline for Point Cloud Registration via Direct Superpoints Matching](https://mlsanity.com/paper/2307.01362/) (2023)

## Abstract (arXiv)

The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the last 10 years to be very useful to perceive the environment in autonomous driving, but few methods exist that directly use these 3D data for odometry. We present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method relies on a scan-to-model matching framework. We first have a specific sampling strategy based on the LiDAR scans. We then define our model as the previous localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface representation. We show experiments with the Velodyne HDL32 with only 0.40% drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after 4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and ranked among the best methods (against mono, stereo and LiDAR methods) with a global drift of only 0.69%.