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Open AccessArticle

Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation

1
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
Navigation and Control Technology Institute of NORINCO Group, Beijing 100089, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 4945; https://doi.org/10.3390/s19224945
Received: 16 October 2019 / Revised: 8 November 2019 / Accepted: 10 November 2019 / Published: 13 November 2019
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: similarity transformation; incremental pose map; monocular vision SLAM; bag-of-words; sparse direct method; histogram equalization; probability graph similarity transformation; incremental pose map; monocular vision SLAM; bag-of-words; sparse direct method; histogram equalization; probability graph
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MDPI and ACS Style

Liu, W.; Wu, S.; Wu, Z.; Wu, X. Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation. Sensors 2019, 19, 4945. https://doi.org/10.3390/s19224945

AMA Style

Liu W, Wu S, Wu Z, Wu X. Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation. Sensors. 2019; 19(22):4945. https://doi.org/10.3390/s19224945

Chicago/Turabian Style

Liu, Wenlei; Wu, Sentang; Wu, Zhongbo; Wu, Xiaolong. 2019. "Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation" Sensors 19, no. 22: 4945. https://doi.org/10.3390/s19224945

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