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Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry

1
School of Automation, Central South University, Changsha 410017, China
2
School of Resources and Safety Engineering, Central South University, Changsha 410017, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Luigi Spagnolo
Sensors 2022, 22(13), 4749; https://doi.org/10.3390/s22134749
Received: 6 May 2022 / Revised: 17 June 2022 / Accepted: 20 June 2022 / Published: 23 June 2022
(This article belongs to the Section Electronic Sensors)
In this paper, we propose a visual marker-aided LiDAR/IMU/encoder integrated odometry, Marked-LIEO, to achieve pose estimation of mobile robots in an indoor long corridor environment. In the first stage, we design the pre-integration model of encoder and IMU respectively to realize the pose estimation combined with the pose estimation from the second stage providing prediction for the LiDAR odometry. In the second stage, we design low-frequency visual marker odometry, which is optimized jointly with LiDAR odometry to obtain the final pose estimation. In view of the wheel slipping and LiDAR degradation problems, we design an algorithm that can make the optimization weight of encoder odometry and LiDAR odometry adjust adaptively according to yaw angle and LiDAR degradation distance respectively. Finally, we realize the multi-sensor fusion localization through joint optimization of an encoder, IMU, LiDAR, and camera measurement information. Aiming at the problems of GNSS information loss and LiDAR degradation in indoor corridor environment, this method introduces the state prediction information of encoder and IMU and the absolute observation information of visual marker to achieve the accurate pose of indoor corridor environment, which has been verified by experiments in Gazebo simulation environment and real environment. View Full-Text
Keywords: integrated odometry; pre-integration; visual marker; multi-sensor fusion integrated odometry; pre-integration; visual marker; multi-sensor fusion
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MDPI and ACS Style

Chen, B.; Zhao, H.; Zhu, R.; Hu, Y. Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry. Sensors 2022, 22, 4749. https://doi.org/10.3390/s22134749

AMA Style

Chen B, Zhao H, Zhu R, Hu Y. Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry. Sensors. 2022; 22(13):4749. https://doi.org/10.3390/s22134749

Chicago/Turabian Style

Chen, Baifan, Haowu Zhao, Ruyi Zhu, and Yemin Hu. 2022. "Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry" Sensors 22, no. 13: 4749. https://doi.org/10.3390/s22134749

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