Abstract
Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry.
Highlights
Positioning and navigation have attracted much attention in recent years, and many achievements have been made in the fields of robotics, micro aircraft, and autonomous driving
We propose a tightly-coupled, optimization-based monocular visual-inertial odometry where inertial measurement units (IMU) measurements, point features, and structural features are used as observation information
We evaluate the performance of the proposed Manhattan world based visual-inertial odometry (VIO) system on the public benchmark datasets and in the mobile phone based indoor field tests
Summary
Positioning and navigation have attracted much attention in recent years, and many achievements have been made in the fields of robotics, micro aircraft, and autonomous driving. Some previous works use the structural regularity of the MW on monocular [22,23,24,25], stereo [26] and RGB-D cameras [27,28], respectively, essentially using the orthogonality of vanishing points to calculate accurate rotation or constrain the relative rotation between frames From these works, it can be seen that the structural feature can eliminate the accumulative rotation drift of the system. As far as we know, this is the first to add structural regularity constraint to VIO in the form of an orthogonal basis It can run in real-time on an Android phone with Kirin 990 5G processor at an average processing speed of 28.1 ms for a single frame
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