StreamVLO: Streaming Visual-LiDAR Odometry with Cumulative Drift Compensation
Mengmeng Liu, Jiuming Liu, Michael Ying Yang, Chaokang Jiang, Jiangtao Li, Yunpeng Zhang, Hesheng Wang, Francesco Nex
Abstract
We propose StreamVLO, a streaming visual-LiDAR odometry framework that performs unified spatio-temporal correlation with Mamba models and tackles the long-standing cumulative drift problem via an online Cumulative Drift Compensation scheme for localization in 4D dynamic environments. Specifically, StreamVLO introduces a unified spatio-temporal correlation module built on Mamba to fuse heterogeneous visual and LiDAR cues across multi-frame clips, overcoming the limited temporal exploration of prior pairwise methods. Furthermore, a Cumulative Drift Compensation module minimizes cumulative drift by iteratively learning residual corrections from multiple historical frames in a causal manner. To strengthen spatial feature representation on salient regions, we adopt a Keypoint-Aware Auxiliary Loss with a winner-takes-all strategy. StreamVLO achieves state-of-the-art performance on two commonly used autonomous driving datasets, reducing errors by 19% t_rel and 22% r_rel on KITTI, and by 18% ATE and 16% RPE on Argoverse, while remaining suitable for real-time deployment.
BibTeX
@inproceedings{cvpr2026_streamvlostreami,
title = {StreamVLO: Streaming Visual-LiDAR Odometry with Cumulative Drift Compensation},
author = {Mengmeng Liu and Jiuming Liu and Michael Ying Yang and Chaokang Jiang and Jiangtao Li and Yunpeng Zhang and Hesheng Wang and Francesco Nex and Hao Cheng},
booktitle = {CVPR 2026},
year = {2026}
}