孙龙龙,江 明,焦传佳.基于运动矢量的改进视觉 SLAM 算法[J].电子测量与仪器学报,2020,34(9):23-31 |
基于运动矢量的改进视觉 SLAM 算法 |
Improved visual SLAM algorithm based on the motion vector |
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DOI: |
中文关键词: 同时定位与地图构建 运动矢量 运动点检测 闭环检测 |
英文关键词:simultaneous localization and mapping motion vector motion point detection loop closure detection |
基金项目:国家自然科学基金(61271377)资助项目 |
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Author | Institution |
Sun Longlong | 1. School of Electrical Engineering, Anhui Polytechnic University,2. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment,Ministry of Education, Anhui Polytechnic University |
Jiang Ming | 1. School of Electrical Engineering, Anhui Polytechnic University,2. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment,Ministry of Education, Anhui Polytechnic University |
Jiao Chuanjia | 1. School of Electrical Engineering, Anhui Polytechnic University,2. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment,Ministry of Education, Anhui Polytechnic University |
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中文摘要: |
针对移动机器人运行场景中出现运动物体时,视觉同时定位与地图构建( SLAM)算法位姿估计误差大且构建地图不一
致的问题,提出了一种基于特征点运动矢量的改进视觉 SLAM 算法。 首先,引入基于特征点运动矢量的运动点检测算法。 通过
结合初始相机位姿,计算图像特征点的运动矢量,并使用期望最大化方法求解运动矢量角度的高斯混合模型参数,通过结合前
一帧的运动点检测结果,从而区分当前图像中的运动特征点;其次,基于运动点检测结果,对当前帧相机位姿进行优化;再次,通
过设置图像预处理环节,剔除运动点占比较大和与前一帧相似性较高的图像,提高闭环检测算法的计算效率;最后,使用剔除动
态点后的图像特征点对场景进行描述,并改进单个节点处图像间相似性得分计算函数,经过闭环确认后,得到正确闭环。 数据
集实验表明,所提算法具有较高的位姿估计精度和较好的鲁棒性,同时能有效检测场景中闭环的存在,且建图效果较好。 |
英文摘要: |
Aiming at the problem that the simultaneous localization and mapping ( SLAM) algorithm has a large pose error and
inconsistent map construction when a moving object appears in mobile robot’ s operating scene, an improved visual SLAM algorithm
based on feature point motion vector is proposed. Firstly, the algorithm of motion points based on feature point motion vector is
introduced. The motion vector can be calculated by combining the initial camera pose, and the Gaussian mixture model parameters of its
angle are solved by using the expectation maximization method. And the motion point detection result of the previous frame is used to
distinguish motion points in the current image. Secondly, the camera pose will be optimized based on results of the motion point
detection. Then the image is pre-processed, and images with a number of motion points and higher similarity to the previous frame will
be eliminated, which can improve the calculation efficiency of loop closure detection. Finally, the scene is described by using feature
points after excluding dynamic features, and the similarity score calculation function of two images at a single node is improved. After
loop closure confirmation, the correct loop is obtained. The datasets experimental results show that the improved algorithm has better
robustness and higher accuracy in the pose estimation. And it can effectively detect the existence of loops in the scene and has a good
mapping. |
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