# Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction

arXiv: https://arxiv.org/abs/2203.03749 · HTML: https://mlsanity.com/paper/2203.03749/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2022.

## Built on by (most cited first, 23 of 23)

- [FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry](https://mlsanity.com/paper/2408.14035/) (2024)
- [MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception](https://mlsanity.com/paper/2403.11496/) (2024)
- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence](https://mlsanity.com/paper/2510.26915/) (2025)
- [WildOS: Open-Vocabulary Object Search in the Wild](https://mlsanity.com/paper/2602.19308/) (2026)
- [Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process](https://mlsanity.com/paper/2402.09189/) (2024)
- [T-CBF: Traversability-based Control Barrier Function to Navigate Vertically Challenging Terrain](https://mlsanity.com/paper/2503.06083/) (2025)
- [FMCW-LIO: A Doppler LiDAR-Inertial Odometry](https://mlsanity.com/paper/2609.29374/) (2026)
- [UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition](https://mlsanity.com/paper/2601.05105/) (2026)
- [GaussLite: Online Task-Conditioned 3D Gaussian Splatting for Real-Time Robotic Mapping](https://mlsanity.com/paper/2606.30809/) (2026)
- [IFTD: Image Feature Triangle Descriptor for Loop Detection in Driving Scenes](https://mlsanity.com/paper/2406.07937/) (2024)
- [KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry](https://mlsanity.com/paper/2501.04263/) (2025)
- [CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments](https://mlsanity.com/paper/2505.06483/) (2025)
- [Learning Smooth State-Dependent Traversability from Dense Point Clouds](https://mlsanity.com/paper/2506.04362/) (2025)
- [Semi-distributed Cross-modal Air-Ground Relative Localization](https://mlsanity.com/paper/2511.06749/) (2025)
- [Geometric Multi-Session Map Merging with Learned Local Descriptors](https://mlsanity.com/paper/2512.24384/) (2025)
- [Multimodal embodiment-aware navigation transformer](https://mlsanity.com/paper/2604.19267/) (2026)
- [LeCropFollow: Latent Space Planning for Navigation in Unstructured Crop Fields](https://mlsanity.com/paper/2606.31941/) (2026)
- [Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller](https://mlsanity.com/paper/2607.25728/) (2026)
- [Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation](https://mlsanity.com/paper/2609.17141/) (2026)
- [Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems](https://mlsanity.com/paper/2609.29375/) (2026)
- [DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles](https://mlsanity.com/paper/2609.38873/) (2026)
- [MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision](https://mlsanity.com/paper/2610.12568/) (2026)

## Abstract (arXiv)

Aggressive motions from agile flights or traversing irregular terrain induce motion distortion in LiDAR scans that can degrade state estimation and mapping. Some methods exist to mitigate this effect, but they are still too simplistic or computationally costly for resource-constrained mobile robots. To this end, this paper presents Direct LiDAR-Inertial Odometry (DLIO), a lightweight LiDAR-inertial odometry algorithm with a new coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction. The key to our method lies in the construction of a set of analytical equations which are parameterized solely by time, enabling fast and parallelizable point-wise deskewing. This method is feasible only because of the strong convergence properties in our nonlinear geometric observer, which provides provably correct state estimates for initializing the sensitive IMU integration step. Moreover, by simultaneously performing motion correction and prior generation, and by directly registering each scan to the map and bypassing scan-to-scan, DLIO's condensed architecture is nearly 20% more computationally efficient than the current state-of-the-art with a 12% increase in accuracy. We demonstrate DLIO's superior localization accuracy, map quality, and lower computational overhead as compared to four state-of-the-art algorithms through extensive tests using multiple public benchmark and self-collected datasets.