arXiv Computer Vision By Mingle Zhao, Jiahao Wang, Tianxiao Gao, Chengzhong Xu, Hui Kong

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

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The paper introduces Free-Init, a high‑frequency, resilient initialization framework for LiDAR‑inertial systems that uses FMCW Doppler LiDAR to capture both point range and Doppler velocity. By fusing point‑wise Doppler velocity with inertial measurements, Free‑Init eliminates the need for motion undistortion, excitation motions, and map correspondences during initialization, making it plug‑and‑play for a wide range of initial motions, including stationary, dynamic, and violent movements. Experiments on diverse platforms and motion scenarios demonstrate that Free‑Init achieves fast convergence and high‑frequency performance, delivering outputs exceeding 10 kHz and outperforming existing methods.

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arXiv Computer Vision
Sep 25

FMCW-LIO: A Doppler LiDAR-Inertial Odometry

FMCW-LIO introduces a novel LiDAR‑inertial odometry framework that exploits the Doppler velocity measurements provided by Frequency Modulated Continuous Wave (FMCW) Doppler LiDARs. By incorporating a motion compensation scheme and a Doppler‑aided observation model, the method effectively removes dynamic points and enhances geometric consistency. Experiments across diverse scenes demonstrate that FMCW‑LIO delivers more accurate state estimation and robust static mapping, outperforming existing algorithms in both accuracy and resilience.

By Mingle Zhao, Jiahao Wang, Tianxiao Gao, Chengzhong Xu, Hui Kong
arXiv Computer Vision
3d ago

Temporal-Aware Fusion for Robust Outdoor LiDAR Localization

The paper introduces TempLoc, a Temporal‑aware Localization framework that improves outdoor LiDAR relocalization by leveraging spatio‑temporal consistency across scans. It first predicts point‑wise global coordinates with uncertainties, then estimates inter‑frame correspondences using an attention‑based Prior Coordinate Generation module, and finally fuses these predictions in an uncertainty‑guided manner to produce a more accurate global 6‑DoF pose. Experiments on the NCLT and Oxford RobotCar datasets show that TempLoc significantly outperforms existing state‑of‑the‑art methods.

By Minghang Zhu, Zhijing Wang, Yuxin Guo, Chen Liu, Yongshu Huang, Wen Li, Sheng Ao, Cheng Wang