Building an accurate map is essential for autonomous robot navigation in the environment without GPS. Compared with single-robot, the multiple-robot system has much better performance in terms of accuracy, efficiency and robustness for the simultaneous localization and mapping (SLAM). As a critical component of multiple-robot SLAM, the problem of map merging still remains a challenge. To this end, this article casts it into point set registration problem and proposes an effective map merging method based on the context-based descriptors and correspondence expansion. It first extracts interest points from grid maps by the Harris corner detector. By exploiting neighborhood information of interest points, it automatically calculates the maximum response radius as scale information to compute the context-based descriptor, which includes eigenvalues and normals computed from local structures of each interest point. Then, it effectively establishes origin matches with low precision by applying the nearest neighbor search on the context-based descriptor. Further, it designs a scale-based corresponding expansion strategy to expand each origin match into a set of feature matches, where one similarity transformation between two grid maps can be estimated by the Random Sample Consensus algorithm. Subsequently, a measure function formulated from the trimmed mean square error is utilized to confirm the best similarity transformation and accomplish the coarse map merging. Finally, it utilizes the scaling trimmed iterative closest point algorithm to refine initial similarity transformation so as to achieve accurate merging. As the proposed method considers scale information in the context-based descriptor, it is able to merge grid maps in diverse resolutions. Experimental results on real robot datasets demonstrate its superior performance over other related methods on accuracy and robustness.

中文翻译：

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通过基于上下文的描述符合并不同分辨率的网格图

构建准确的地图对于在没有 GPS 的环境中进行自主机器人导航至关重要。与单机器人相比，多机器人系统在同步定位和建图（SLAM）的准确性、效率和鲁棒性方面具有更好的性能。作为多机器人 SLAM 的关键组成部分，地图合并问题仍然是一个挑战。为此，本文将其转化为点集配准问题，提出了一种基于上下文描述符和对应扩展的有效地图合并方法。它首先通过 Harris 角点检测器从网格图中提取兴趣点。通过利用兴趣点的邻域信息，它自动计算最大响应半径作为尺度信息来计算基于上下文的描述符，其中包括从每个兴趣点的局部结构计算的特征值和法线。然后，它通过对基于上下文的描述符应用最近邻搜索来有效地建立低精度的原点匹配。此外，它设计了一种基于尺度的对应扩展策略，将每个原点匹配扩展为一组特征匹配，其中两个网格图之间的一个相似性变换可以通过随机样本共识算法来估计。随后，利用修剪后的均方误差公式化的度量函数来确定最佳相似变换并完成粗略的地图合并。最后，利用缩放修剪迭代最近点算法对初始相似度变换进行细化，从而实现准确的合并。由于所提出的方法在基于上下文的描述符中考虑了尺度信息，它能够合并不同分辨率的网格图。在真实机器人数据集上的实验结果表明，它在准确性和鲁棒性方面优于其他相关方法。