Accurate Prior-centric Monocular Positioning with Offline LiDAR Fusion

Unmanned vehicles usually rely on Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) sensors to achieve high-precision localization results for navigation purpose. However, this combination with their associated costs and infrastructure demands, poses challenges for widespread a...

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Published in:2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 11934 - 11940
Main Authors: He, Jinhao, Huang, Huaiyang, Zhang, Shuyang, Jiao, Jianhao, Liu, Chengju, Liu, Ming
Format: Conference Proceeding
Language:English
Published: IEEE 13.05.2024
Subjects:
Cameras
Laser radar
Localization
Location awareness
Robot vision systems
Robotics in Under-Resourced Settings
Robustness
Sensor Fusion
Sensor systems
Visualization
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Abstract Unmanned vehicles usually rely on Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) sensors to achieve high-precision localization results for navigation purpose. However, this combination with their associated costs and infrastructure demands, poses challenges for widespread adoption in mass-market applications. In this paper, we aim to use only a monocular camera to achieve comparable onboard localization performance by tracking deep-learning visual features on a LiDAR-enhanced visual prior map. Experiments show that the proposed algorithm can provide centimeter-level global positioning results with scale, which is effortlessly integrated and favorable for low-cost robot system deployment in real-world applications.
AbstractList Unmanned vehicles usually rely on Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) sensors to achieve high-precision localization results for navigation purpose. However, this combination with their associated costs and infrastructure demands, poses challenges for widespread adoption in mass-market applications. In this paper, we aim to use only a monocular camera to achieve comparable onboard localization performance by tracking deep-learning visual features on a LiDAR-enhanced visual prior map. Experiments show that the proposed algorithm can provide centimeter-level global positioning results with scale, which is effortlessly integrated and favorable for low-cost robot system deployment in real-world applications.
Author Jiao, Jianhao
Liu, Chengju
Zhang, Shuyang
Liu, Ming
He, Jinhao
Huang, Huaiyang
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  givenname: Huaiyang
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  givenname: Ming
  surname: Liu
  fullname: Liu, Ming
  organization: The Hong Kong University of Science and Technology (Guangzhou),Thrust of Robotics and Autonomous Systems,Guangzhou,China
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SubjectTerms Cameras
Laser radar
Localization
Location awareness
Robot vision systems
Robotics in Under-Resourced Settings
Robustness
Sensor Fusion
Sensor systems
Visualization
Title Accurate Prior-centric Monocular Positioning with Offline LiDAR Fusion
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