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基于稀疏直接法闭环检测定位的视觉里程计
引用本文:汝少楠,何元烈,叶星余.基于稀疏直接法闭环检测定位的视觉里程计[J].广东工业大学学报,2021,38(3):48-54.
作者姓名:汝少楠  何元烈  叶星余
作者单位:广东工业大学 计算机学院,广东 广州 510006
基金项目:国家自然科学基金资助项目(61876043)
摘    要:视觉里程计在移动机器人的定位导航中发挥着关键性作用,但当前的算法在运行速度、轨迹精度和鲁棒性等方面依然存在改善空间.为提高相机轨迹精度,提出基于稀疏直接法的闭环检测算法.该算法直接提取两种特征组成混合型特征点提升系统鲁棒性,混合型特征点用于跟踪和匹配关键帧,使视觉里程计能够检测闭环,再用位姿图优化提升定位精度.实验结果...

关 键 词:视觉里程计  闭环检测  稀疏直接法  视觉特征  相机轨迹
收稿时间:2020-09-04

Visual Odometry Based on Sparse Direct Method Loop-Closure Detection
Ru Shao-nan,He Yuan-lie,Ye Xing-yu.Visual Odometry Based on Sparse Direct Method Loop-Closure Detection[J].Journal of Guangdong University of Technology,2021,38(3):48-54.
Authors:Ru Shao-nan  He Yuan-lie  Ye Xing-yu
Affiliation:School of Computers, Guangdong University of Technology, Guangzhou 510006, China
Abstract:The visual odometry plays a key role in the positioning and navigation of mobile robots, but the current algorithm still has room for improvement in terms of running speed, trajectory accuracy, and robustness. In order to improve the accuracy of camera trajectory, a loop-closure detection algorithm is proposed based on the sparse direct method. The algorithm directly extracts two features to form a hybrid feature point to improve the robustness of the system. The hybrid feature point is used to track and match key frames, so that the visual odometry can detect closed loops, and then the pose map is used to optimize the positioning accuracy. Experimental results show strong robustness in a complex environment and a balance between speed and accuracy.
Keywords:visual odometry  loop-closure detection  sparse direct method  visual feature  camera trajectory  
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