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:2609.21000v1 Announce Type: cross
Abstract: Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustne...
By Eric Xie, Daniil Lisus, Timothy D. Barfoot
arXiv:2609.07738v1 Announce Type: cross
Abstract: LiDAR-based 3D Single Object Tracking (3D SOT) is critical for robotic perception and navigation and aims to localize dynamic objects across frames i...
By Zhaofeng Hu, Sifan Zhou, Jiahao Nie, Ziyu Zhao, Weizi Li, Ci-jyun Liang
arXiv:2509.06285v2 Announce Type: cross
Abstract: LiDAR point cloud registration is fundamental to robotic perception and navigation. In geometrically degenerate environments (e.g., corridors), regis...
By Xiangcheng Hu, Xieyuanli Chen, Mingkai Jia, Jin Wu, Ping Tan, Steven L. Waslander
arXiv:2608. 19522v1 Announce Type: cross Abstract: Scan-to-map LiDAR odometry drifts unboundedly along the unobservable axes of geometrically degenerate environments like tunnels and corridors, and existing degeneracy handling requires environment-specific parameter tuning.
By Eunsoo Im
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