# Tightly Coupled 3D Lidar Inertial Odometry and Mapping

arXiv: https://arxiv.org/abs/1904.06993 · HTML: https://mlsanity.com/paper/1904.06993/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 30 of 34)

- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge](https://mlsanity.com/paper/2103.11470/) (2021)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2202.12197/) (2022)
- [Greedy-Based Feature Selection for Efficient LiDAR SLAM](https://mlsanity.com/paper/2103.13090/) (2021)
- [Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization](https://mlsanity.com/paper/2105.02010/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras](https://mlsanity.com/paper/2502.19630/) (2025)
- [Intensity Image-based LiDAR Fiducial Marker System](https://mlsanity.com/paper/2203.01816/) (2022)
- [TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation](https://mlsanity.com/paper/2304.07728/) (2023)
- [Learning Parallax for Stereo Event-based Motion Deblurring](https://mlsanity.com/paper/2309.09513/) (2023)
- [MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion](https://mlsanity.com/paper/2108.02448/) (2021)
- [FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization](https://mlsanity.com/paper/2203.00292/) (2022)
- [R3LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator](https://mlsanity.com/paper/2209.03666/) (2022)
- [Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process](https://mlsanity.com/paper/2402.09189/) (2024)
- [FMCW-LIO: A Doppler LiDAR-Inertial Odometry](https://mlsanity.com/paper/2609.29374/) (2026)
- [HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion](https://mlsanity.com/paper/2304.04508/) (2023)
- [Monocular Localization with Semantics Map for Autonomous Vehicles](https://mlsanity.com/paper/2406.03835/) (2024)
- [Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues](https://mlsanity.com/paper/2411.07815/) (2024)
- [AR Mapping: Accurate and Efficient Mapping for Augmented Reality](https://mlsanity.com/paper/2103.14846/) (2021)
- [3D Surfel Map-Aided Visual Relocalization with Learned Descriptors](https://mlsanity.com/paper/2104.03856/) (2021)
- [SmartDepthSync: Open Source Synchronized Video Recording System of Smartphone RGB and Depth Camera Range Image Frames with Sub-millisecond Precision](https://mlsanity.com/paper/2111.03552/) (2021)
- [LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM](https://mlsanity.com/paper/2209.08810/) (2022)
- [A Tightly Coupled LiDAR-IMU Odometry through Iterated Point-Level Undistortion](https://mlsanity.com/paper/2209.12249/) (2022)
- [Perception-and-Energy-aware Motion Planning for UAV using Learning-based Model under Heteroscedastic Uncertainty](https://mlsanity.com/paper/2309.14272/) (2023)
- [AscDAMs: Advanced SLAM-based channel detection and mapping system](https://mlsanity.com/paper/2401.13877/) (2024)
- [Universal Online Temporal Calibration for Optimization-based Visual-Inertial Navigation Systems](https://mlsanity.com/paper/2501.01788/) (2025)
- [KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry](https://mlsanity.com/paper/2501.04263/) (2025)
- [Deep Neural Networks for Accurate Depth Estimation with Latent Space Features](https://mlsanity.com/paper/2502.11777/) (2025)
- [CAR-LOAM: Color-Assisted Robust LiDAR Odometry and Mapping](https://mlsanity.com/paper/2502.17249/) (2025)

## Abstract (arXiv)

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.