Abstract
The novel contribution of this paper is to propose an incremental pose map optimization for monocular vision simultaneous localization and mapping (SLAM) based on similarity transformation, which can effectively solve the scale drift problem of SLAM for monocular vision and eliminate the cumulative error by global optimization. With the method of mixed inverse depth estimation based on a probability graph, the problem of the uncertainty of depth estimation is effectively solved and the robustness of depth estimation is improved. Firstly, this paper proposes a method combining the sparse direct method based on histogram equalization and the feature point method for front-end processing, and the mixed inverse depth estimation method based on a probability graph is used to estimate the depth information. Then, a bag-of-words model based on the mean initialization K-means is proposed for closed-loop feature detection. Finally, the incremental pose map optimization method based on similarity transformation is proposed to process the back end to optimize the pose and depth information of the camera. When the closed loop is detected, global optimization is carried out to effectively eliminate the cumulative error of the system. In this paper, indoor and outdoor environmental experiments are carried out using open data sets, such as TUM and KITTI, which fully proves the effectiveness of this method. Closed-loop detection experiments using hand-held cameras verify the importance of closed-loop detection. This method can effectively solve the scale drift problem of monocular vision SLAM and has strong robustness.
Highlights
With the continuous development of artificial intelligence technology, research of visual slam has made great progress, and new achievements are emerging
In the back-end to propose a method of incremental pose optimization based on similarity transformation, which takes processing, the main innovation is to propose a method of incremental pose optimization based on the uncertainty of scale intowhich account to the ensure the consistency monocular vision slam
This paper presented an incremental pose map optimization method based on the similarity transformation group, which effectively solves the problems of rotation, translation, and scale drift in closed-loop detection
Summary
With the continuous development of artificial intelligence technology, research of visual slam has made great progress, and new achievements are emerging. Aiming at the uncertainty of depth measurement by the monocular camera, an incremental pose map optimization method based on similarity transformation is proposed by using the inverse depth estimation method based on a probability graph for scale drift. In [16], an open-square smoothing filtering algorithm was proposed to estimate the pose of the camera This method decomposes the correlation information matrix or the measured Jacobian matrix into square roots, so the calculation speed is faster, and the accuracy is higher. Zhou et al [28] proposed a semi-dense monocular simultaneous location and mapping (SLAM) method that can deal with pure camera rotation motion, and established a probabilistic depth map model based on Bayesian estimation.
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