Abstract
In this article, we present novel solutions to estimate the ego-motion of a multi-camera system with a known vertical direction (e.g., from the inertial measurement unit). By assuming small camera motion between successive video frames, we demonstrate that rotation and translation estimation can be decoupled. This makes our methods require fewer correspondences to estimate the ego-motion and have a good accuracy. Accordingly, we estimate the ego-motion with two steps. First, we propose a 1-point method to estimate rotation with only a single correspondence which produces up to two solutions. Then, we adopt a 3-point linear method and a 2-point sampling method to solve translation which produce a single solution. We compared our algorithms with state-of-the-art algorithms on synthetic and real datasets. The experiments demonstrate that our algorithms are accurate and efficient in road driving scenarios. We also demonstrate that our proposed methods can efficiently find an optimal inlier set using histogram voting or exhaustive search instead of RANSAC.
Highlights
T HE relative pose estimation problem is classical and fundamental in computer vision applications, such as robotics, automotive industry, augmented reality, and visual simultaneous localization and mapping
Our work aims at solving the ego-motion estimation problem for a multi-camera system when the vertical direction in the multi-camera coordinate frame is provided by the inertial measurement unit (IMU) [13], [20]
In this paper, we proposed new methods to solve the problem of ego-motion estimation of a multi-camera system with decoupled rotation and translation estimation, while the vertical direction is known
Summary
T HE relative pose estimation problem is classical and fundamental in computer vision applications, such as robotics, automotive industry, augmented reality, and visual simultaneous localization and mapping. As the accuracy of the yaw angle from the IMU sensor is not as good as those of the roll and pitch angles, we use the roll and pitch angles to determine the vertical direction, which reduces the degrees of freedom (DOFs) in the relative pose by two This makes the ego-motion estimation process simpler and faster. Our work aims at solving the ego-motion estimation problem for a multi-camera system when the vertical direction in the multi-camera coordinate frame is provided by the IMU [13], [20]. We propose a 1-point method to estimate rotation for multi-camera systems on the condition of knowing the vertical direction. We propose two methods to estimate translation for multi-camera systems, 3-point linear method and 2point sampling method, which have high accuracy.
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