POL-VIO: A Visual Inertial Odometry With Polarization Skylight Compass in Challenge Environment

Visual-inertial odometry with low-cost visual and inertial sensors can provide accurate navigation results in GNSS-denied environment. However, the mainstream methods currently suffer from the inevitable accumulation of position and heading errors over long-term operation. Inspired by the compound eyes of insects such as ants, polarization skylight compass can obtain the accurate orientation from the sky polarization pattern without cumulative error. This letter proposes a complete polarization-based visual-inertial odometry (POL-VIO) for challenge environment. This system offers a visual-inertial odometry enhanced by a skylight compass, which reduces heading drift and provides high-accuracy state estimation under complex environment. Additionally, it achieves online spatio-temporal calibration of the polarization light compass, incorporating ambiguity correction and horizontal angle compensation in polarized light orientation. Besides, we utilize the degree of skylight polarization to determine whether compass measurements are reliable. The polarization skylight compass enhances the reliability of heading constraints in complex environments by utilizing the Angle of Polarization information. Results from real-world experiments substantiate that our system can achieve a notable improvement in positioning and orientation accuracy under significant obstructed sky observations after long time of operation. On three datasets with a total length exceeding 7 km, the average positioning error of POL-VIO is less than 2 m/km.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-06-06
DOI
https://doi.org/10.1109/lra.2025.3577428
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00

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article

POL-VIO: A Visual Inertial Odometry With Polarization Skylight Compass in Challenge Environment

Xiaofeng He, Huaiyi Zhang, Lianwei Teng, Wenzhou Zhou et al.
IEEE Robotics and Automation Letters
Robotics and Sensor-Based Localization
article

POL-VIO: A Visual Inertial Odometry With Polarization Skylight Compass in Challenge Environment

Xiaofeng He, Huaiyi Zhang, Lianwei Teng, Wenzhou Zhou, Lilian Zhang, Chen Fan
article en

Abstract

Visual-inertial odometry with low-cost visual and inertial sensors can provide accurate navigation results in GNSS-denied environment. However, the mainstream methods currently suffer from the inevitable accumulation of position and heading errors over long-term operation. Inspired by the compound eyes of insects such as ants, polarization skylight compass can obtain the accurate orientation from the sky polarization pattern without cumulative error. This letter proposes a complete polarization-based visual-inertial odometry (POL-VIO) for challenge environment. This system offers a visual-inertial odometry enhanced by a skylight compass, which reduces heading drift and provides high-accuracy state estimation under complex environment. Additionally, it achieves online spatio-temporal calibration of the polarization light compass, incorporating ambiguity correction and horizontal angle compensation in polarized light orientation. Besides, we utilize the degree of skylight polarization to determine whether compass measurements are reliable. The polarization skylight compass enhances the reliability of heading constraints in complex environments by utilizing the Angle of Polarization information. Results from real-world experiments substantiate that our system can achieve a notable improvement in positioning and orientation accuracy under significant obstructed sky observations after long time of operation. On three datasets with a total length exceeding 7 km, the average positioning error of POL-VIO is less than 2 m/km.

IEEE Robotics and Automation LettersVol. 10(11)
National University of Defense Technology (CN)
National Natural Science Foundation of China, Hunan Provincial Innovation Foundation for Postgraduate
Climate action
Openalex Percentile: Top 83%
Robotics and Sensor-Based Localization
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