Optimization Algorithm of Visual Odometry for SLAM Based on Local Image Entropy

被引：0
|
作者：
Yu Y.-N. [1 ]
Wei H. [2 ]
Chen J. [1 ]
机构：
[1] School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin
[2] Department of Computer Science, University of Reading, Reading
来源：
Yu, Ya-Nan (jesuisyyn@126.com) | 1600年 / Science Press卷 / 47期
基金：
中国国家自然科学基金;
关键词：
Information entropy; Pose-graph optimization; SLAM; Sparse feature; Visual odometry;
D O I：
10.16383/j.aas.c180278
中图分类号：
学科分类号：
摘要：
For the problems of failed matching and tracking loss due to big camera rotation in simultaneous localization and mapping (SLAM) for mobile robots, an optimized detail enhancement algorithm of visual odometry based on the local image entropy is proposed. The image pyramid is built and is divided into blocks on each level to extract features homogeneously. The information of each image block is determined through its entropy value and the blocks with low contrast and small intensity gradient will be deleted to reduce feature calculation. Nonlinear and adaptive illumination adjustment on each reserved block is applied to increase local image details. Local features that representing image information is preserved as much as possible to be the correlations between adjacent frames and keyframes. Combined with the pose graph optimization method, the local and global optimization of accumulation error is carried out to further improve the system performance for mobile robot. The proposed method is verified on the TUM dataset. Since using the feature properties which are more reflective of texture and shape, the maximum success rate of motion tracking is increased to over 60 %. And the results also show that the tracking error, translational error and rotation error is decreased. Compared with the original system ORB-SLAM2, this method can not only improve visual positioning accuracy of the mobile robot, but also meet the application need of real-time SLAM requirement. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：1460 / 1466
页数：6
相关论文
共 22 条
  • [1] Fink G K, Franke M, Lynch A F, Robenack K, Godbolt B., Visual inertial SLAM: Application to unmanned aerial vehicle, IFAC-PapersOnline, 50, 1, pp. 1965-1970, (2017)
  • [2] Holl M, Lepetit V., Monocular LSD-SLAM Integration within AR System, (2017)
  • [3] Wang Nan, Ma Shu-Gen, Li Bin, Wang Ming-Hui, Zhao Ming-Yang, A model transformation of map representation for hierarchical SLAM that can be used for after-earthquake buildings, Acta Automatica Sinica, 41, 10, pp. 1723-1733, (2015)
  • [4] Zhao Yang, Liu Guo-Liang, Tian Guo-Hui, Luo Yong, Wang Zi-Ren, Zhang Wei, Li Jun-Wei, A survey of visual SLAM based on deep learning, Robot, 39, 6, pp. 889-896, (2017)
  • [5] Yi K M, Trulls E, Lepetit V, Fua P., LIFT: Learned invariant feature transform, Proceedings of the 14th European Conference on Computer Vision, 9910, pp. 467-483, (2016)
  • [6] DeTone D, Malisiewicz T, Rabinovich A., Toward geometric deep SLAM, Computer Vision and Pattern Recognition, (2017)
  • [7] Ding Wen-Dong, Xu De, Liu Xi-Long, Zhang Da-Peng, Chen Tian, Review on visual odometry for mobile robots, Acta Automatica Sinica, 44, 3, pp. 385-400, (2018)
  • [8] Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard J J., Past, present, and future of simultaneous localization and mapping: Towards the robust-perception age, IEEE Transactions on Robotics, 32, 6, pp. 1309-1332, (2016)
  • [9] Kim J, Kim A., Light condition invariant visual SLAM via entropy based image fusion, Proceedings of the 14th International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 529-533, (2017)
  • [10] Davison A J, Reid I D, Molton N D, Stasse O., MonoSLAM: Real-time single camera SLAM, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 6, pp. 1052-1067, (2007)
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