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A Grid Map Fusion Algorithm Based on Maximum Common Subgraph

机译：基于最大公共子图的网格地图融合算法

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摘要

Grid map fusion is a key problem for multi-robot simultaneous localization and mapping (SLAM), and single robot graph-SLAM. To solve this problem, this paper proposes a novel approach to fuse maps based on maximum common subgraph without the requirements of any assumption on the robot's pose. In the approach, the problem of grid map fusion is transformed into an image registration problem, and then the objective function is established to calculate a transformation matrix. Firstly, a corner extraction algorithm is applied to extract corner points from grid maps. Secondly, according to the extracted corner points the isomorphism scheme of triangle is constructed, as a result the initial optimal transformation matrix to being calculated. Finally, the transformation matrix is optimized by finding a maximum common subgraph based on iterating the polygon isomorphism scheme with more corner points. Experimental results show that the proposed approach can solve the grid map merging problem high accurately and robustly.
机译：网格地图融合是多机器人同时定位和映射（SLAM）和单机器人图SLAM的关键问题。为了解决这个问题，本文提出了一种新颖的基于最大公共子图的融合图映射方法，而无需对机器人的姿势进行任何假设。该方法将栅格地图融合问题转化为图像配准问题，然后建立目标函数来计算变换矩阵。首先，采用角点提取算法从网格地图中提取角点。其次，根据提取的拐角点构造三角形的同构方案，从而计算出初始最优变换矩阵。最后，通过迭代具有更多拐角点的多边形同构方案，通过找到最大公共子图来优化变换矩阵。实验结果表明，该方法可以高精度，鲁棒性地解决网格图合并问题。

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