Multi-Robot Point Cloud Map Fusion Algorithm Based on Visual SLAM

Yanjiang Chen, Yanbo Wang, Junqin Lin, Zhihong Chen, Yao Wang
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Abstract

To solve the problem of inaccurate judgment of overlapping areas in multi-robot system point cloud map fusion, an overlapping areas judgment method based on visual SLAM key frames relative motion size is mooted. On the basis of SLAM mapping, the relative motion is determined comprehensively through features matching and geometric constraint between key frames. Then overlapping areas of maps are determined by relative motion size. At last initial transformation matrix between maps can be calculated. We run our algorithm in both open datasets and real world environment. The results show that the accuracy of this algorithm is higher than that of traditional algorithms.
基于视觉SLAM的多机器人点云图融合算法
针对多机器人系统点云图融合中重叠区域判断不准确的问题,提出了一种基于视觉SLAM关键帧相对运动大小的重叠区域判断方法。在SLAM映射的基础上,通过关键帧之间的特征匹配和几何约束综合确定相对运动。然后根据相对运动大小确定地图的重叠区域。最后计算出映射间的初始变换矩阵。我们在开放数据集和现实环境中运行我们的算法。结果表明,该算法的精度高于传统算法。
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