论文标题
视觉大满贯的地图合并算法：可行性研究和经验评估
Map-merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation
论文作者
论文摘要
同时进行本地化和映射，尤其是仅依靠视频数据（VSLAM）的映射，这是一个充满挑战的问题，在机器人技术和计算机视觉中已经进行了广泛的研究。最先进的VSLAM算法能够构建准确的地图，使移动机器人能够自动浏览未知环境。在这项工作中，我们对与VSLAM有关的一个重要问题感兴趣，即MAP合并，该问题可能出现在各种实际上重要的情况下，例如在多机器人覆盖方案中。这个问题询问是否可以将不同的VSLAM地图合并为一致的单一表示。我们检查了现有的2D和3D地图合并算法，并在现实的模拟环境（栖息地）中进行了广泛的经验评估。进行定性和定量比较，并报告并分析获得的结果。
Simultaneous localization and mapping, especially the one relying solely on video data (vSLAM), is a challenging problem that has been extensively studied in robotics and computer vision. State-of-the-art vSLAM algorithms are capable of constructing accurate-enough maps that enable a mobile robot to autonomously navigate an unknown environment. In this work, we are interested in an important problem related to vSLAM, i.e. map merging, that might appear in various practically important scenarios, e.g. in a multi-robot coverage scenario. This problem asks whether different vSLAM maps can be merged into a consistent single representation. We examine the existing 2D and 3D map-merging algorithms and conduct an extensive empirical evaluation in realistic simulated environment (Habitat). Both qualitative and quantitative comparison is carried out and the obtained results are reported and analyzed.
