ColorMap-VIO: A Drift-Free Visual-Inertial Odometry in a Prior Colored Point Cloud Map
Jie Xu, Xuanxuan Zhang, Yongxin Ma, Yixuan Li, Linji Wang, Xinhang Xu, Shenghai Yuan, Lihua Xie
Abstract
Visual-inertial odometry (VIO) can estimate robot poses at high frequencies but suffers from accumulated drift over time. Incorporating point cloud maps offers a promising solution, yet existing registration methods between vision and point clouds are limited by heterogeneous feature alignment, leaving much information underutilized and resulting in reduced accuracy, poor robustness, and high computational cost. To address these challenges, this paper proposes a visual-inertial localization system based on color point cloud maps, consisting of two main components: color map construction and visual-inertial tracking. A gradient-based map sparsification strategy is employed during map construction to preserve salient features while reducing storage and computation. For localization, we propose an image pyramid-based visual photometric IESKF, which fuses IMU and photometric observations to estimate precise poses. Gradient-rich feature points are projected onto image pyramids across multiple resolutions to perform iterative updates, effectively avoiding local minima and improving accuracy. Experimental results show that our method achieves stable and accurate localization bounded by map precision, and demonstrates higher efficiency and robustness than existing map-based approaches.
BibTeX
@inproceedings{ral2026_colormapvioadrif,
title = {ColorMap-VIO: A Drift-Free Visual-Inertial Odometry in a Prior Colored Point Cloud Map},
author = {Jie Xu and Xuanxuan Zhang and Yongxin Ma and Yixuan Li and Linji Wang and Xinhang Xu and Shenghai Yuan and Lihua Xie},
booktitle = {RA-L 2026},
year = {2026}
}