Visual-Inertial Localization With Prior LiDAR Map Constraints
Xingxing Zuo, Patrick Geneva, Yulin Yang, Wenlong Ye, Yong Liu, Guoquan Huang
Abstract
In this letter, we develop a low-cost stereo visual-inertial localization system, which leverages efficient multi-state constraint Kalman filter (MSCKF)-based visual-inertial odometry (VIO) while utilizing an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> LiDAR map to provide bounded-error three-dimensional navigation. Besides the standard sparse visual feature measurements used in VIO, the global registrations of visual semi-dense clouds to the prior LiDAR map are also exploited in a tightly-coupled MSCKF update, thus correcting accumulated drift. This cross-modality constraint between visual and LiDAR pointclouds is particularly addressed. The proposed approach is validated on both Monte Carlo simulations and real-world experiments, showing that LiDAR map constraints between clouds created through different sensing modalities greatly improve the standard VIO and provide bounded-error performance.
BibTeX
@inproceedings{ral2019_visualinertiallo,
title = {Visual-Inertial Localization With Prior LiDAR Map Constraints},
author = {Xingxing Zuo and Patrick Geneva and Yulin Yang and Wenlong Ye and Yong Liu and Guoquan Huang},
booktitle = {RA-L 2019},
year = {2019}
}