Mixed Probability Inverse Depth Estimation Based on Probabilistic Graph Model

• Main Authors: Wenlei Liu, Sentang Wu, Xiaolong Wu, Kai Li
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Mixed probability distribution model; factor graph; inverse depth estimation; Lie group and Lie algebra; fundamental matrix; camera pose
• Online Access: https://ieeexplore.ieee.org/document/8730310/

In this paper, a mixed probability inverse depth estimation method based on probabilistic graph model is proposed, which can effectively solve the problems of far distance from the camera center and long data tail in depth estimation. At the same time, not only the accuracy can be improved but also the robustness of inverse depth estimation can be developed. First, the triangle method was used to find the depth information and location of a point in space, and the inverse depth information was obtained as the initial information of inverse depth estimation. Then, the basic matrix in epipolar geometry was obtained by using the normalized eight-point algorithm, and the pose of a camera was obtained as the initial information of optimization. Next, the pose of the monocular camera was modeled by a factor graph model, and the pose estimation was transformed into an unconstrained optimization problem by using the transformation relationship between Lie group and Lie algebra to obtain the pose of the camera. Finally, the inverse depth obtained by using the Gauss-uniform mixed probability distribution based on the probability graph model was used to calculate the recurrence formula by approximate inference, which can facilitate the sequential processing of multiple images. The depth information was quantitatively measured and compared by using TUM datasets, and the length of space object was measured by using inverse depth information, thus the measurement accuracy of this method was indirectly verified. This method is characterized by strong robustness and high measurement accuracy in the environments with random interferences.