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

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 estimati...

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Autores principales: Chen, Baifan, Zhao, Haowu, Zhu, Ruyi, Hu, Yemin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269198/
https://www.ncbi.nlm.nih.gov/pubmed/35808241
http://dx.doi.org/10.3390/s22134749
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author Chen, Baifan
Zhao, Haowu
Zhu, Ruyi
Hu, Yemin
author_facet Chen, Baifan
Zhao, Haowu
Zhu, Ruyi
Hu, Yemin
author_sort Chen, Baifan
collection PubMed
description 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.
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spelling pubmed-92691982022-07-09 Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry Chen, Baifan Zhao, Haowu Zhu, Ruyi Hu, Yemin Sensors (Basel) Article 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. MDPI 2022-06-23 /pmc/articles/PMC9269198/ /pubmed/35808241 http://dx.doi.org/10.3390/s22134749 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Baifan
Zhao, Haowu
Zhu, Ruyi
Hu, Yemin
Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry
title Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry
title_full Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry
title_fullStr Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry
title_full_unstemmed Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry
title_short Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry
title_sort marked-lieo: visual marker-aided lidar/imu/encoder integrated odometry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269198/
https://www.ncbi.nlm.nih.gov/pubmed/35808241
http://dx.doi.org/10.3390/s22134749
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