# Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems

arXiv: https://arxiv.org/abs/2609.29375 · HTML: https://mlsanity.com/paper/2609.29375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020)
- [HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatiotemporal Variations](https://mlsanity.com/paper/2309.14590/) (2023)
- [Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction](https://mlsanity.com/paper/2203.03749/) (2022)
- [LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation](https://mlsanity.com/paper/1907.02233/) (2019)
- [Efficient and Consistent Bundle Adjustment on Lidar Point Clouds](https://mlsanity.com/paper/2209.08854/) (2022)
- [DICP: Doppler Iterative Closest Point Algorithm](https://mlsanity.com/paper/2201.11944/) (2022)
- [Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation](https://mlsanity.com/paper/2007.14759/) (2020)
- [Robust Real-time LiDAR-inertial Initialization](https://mlsanity.com/paper/2202.11006/) (2022)
- [LiDAR Odometry Survey: Recent Advancements and Remaining Challenges](https://mlsanity.com/paper/2312.17487/) (2023)
- [Kalman Filters on Differentiable Manifolds](https://mlsanity.com/paper/2102.03804/) (2021)
- [Need for Speed: Fast Correspondence-Free Lidar-Inertial Odometry Using Doppler Velocity](https://mlsanity.com/paper/2303.06511/) (2023)
- [Robots State Estimation and Observability Analysis Based on Statistical Motion Models](https://mlsanity.com/paper/2010.05957/) (2020)

## Abstract (arXiv)

Robust initialization is crucial for online systems. In the letter, a high-frequency and resilient initialization framework is designed for LiDAR-inertial systems, leveraging both inertial sensors and Doppler LiDAR. The innovative FMCW Doppler LiDAR opens up a novel avenue for robotic sensing by capturing not only point range but also Doppler velocity via the intrinsic Doppler effect. By fusing point-wise Doppler velocity with inertial measurements under non-inertial kinematics, the proposed framework, Free-Init, eliminates reliance on motion undistortion of LiDAR scans, excitation motions, and map correspondences during the initialization phase. Free-Init is also plug-and-play compatible with typical LiDAR-inertial systems and is versatile to handle a wide range of initial motions when the system starts, including stationary, dynamic, and even violent motions. The embedded Doppler-inertial velocimeter ensures fast convergence and high-frequency performance, delivering outputs exceeding 10 kHz. Comprehensive experiments on diverse platforms and across myriad motion scenes validate the framework's effectiveness. The results demonstrate the superior performance of Free-Init, highlighting the necessity of fast, resilient, and dynamic initialization for online systems.