Robust SRIF-based LiDAR-IMU Localization for Autonomous Vehicles
Kun Li1; Zhanpeng Ouyang2; Lan Hu2; Dayang Hao1; Laurent Kneip2
2021
Source PublicationIEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
Status正式接收
DOI-
Abstract

We present a tightly-coupled multi-sensor fusion architecture for autonomous vehicle applications, which achieves centimetre-level accuracy and high robustness in various scenarios. In order to realize robust and accurate point-cloud feature matching we propose a novel method for extracting structural, highly discriminative features from LiDAR point clouds. For high frequency motion prediction and noise propagation, we use incremental on-manifold IMU preintegration. We also adopt a multi-frame sliding window square root inverse filter, so that the system maintains numerically stable results under the premise of limited power consumption. To verify our methodology, we test the fusion algorithm in multiple applications and platforms equipped with a LiDAR-IMU system. Our results demonstrate that our fusion framework attains state-of-the-art localization accuracy, high robustness and a good generalization ability

Author of SourceICRA
Document Type会议论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/126753
Collection信息科学与技术学院_硕士生
信息科学与技术学院_PI研究组_Laurent Kneip组
信息科学与技术学院_博士生
Affiliation1.Damo Academy, Alibaba Group
2.Mobile Perception Lab, SIST, ShanghaiTech
Recommended Citation
GB/T 7714
Kun Li,Zhanpeng Ouyang,Lan Hu,et al. Robust SRIF-based LiDAR-IMU Localization for Autonomous Vehicles[C]//ICRA,2021.
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