Map completion from partial observation using the global structure of multiple environmental maps
Map completion from partial observation using the global structure of multiple environmental maps
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DOI：
10.1080/01691864.2022.2029762
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发表时间：
2021-03
影响因子：
2
通讯作者：
Y. Katsumata;Akinori Kanechika;Akira Taniguchi;Lotfi El Hafi;Y. Hagiwara;T. Taniguchi
中科院分区：
文献类型：
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作者：
Y. Katsumata;Akinori Kanechika;Akira Taniguchi;Lotfi El Hafi;Y. Hagiwara;T. Taniguchi
将各种室内环境的空间结构作为先验知识，机器人将更有效地构造地图。自主移动机器人通常应用同时定位和映射（SLAM）方法来了解新访问的环境中的可触及区域。但是，传统的映射方法仅通过考虑传感器观察和控制信号来估算当前环境图的限制。本文提出了一种新颖的SLAM方法，即基于MAP完成网络的SLAM（MCN-SLAM），该方法基于概率生成模型，该模型结合了深层神经网络以完成地图的完成。这些地图完成网络主要在生成对抗网络（GAN）的框架中培训，以提取大量现有地图数据的全局结构。我们在实验中表明，所提出的方法可以估计环境图在部分观察情况下比以前的SLAM方法好1.3倍。图形摘要
Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently. Autonomous mobile robots generally apply simultaneous localization and mapping (SLAM) methods to understand the reachable area in newly visited environments. However, conventional mapping approaches are limited by only considering sensor observation and control signals to estimate the current environment map. This paper proposes a novel SLAM method, map completion network-based SLAM (MCN-SLAM), based on a probabilistic generative model incorporating deep neural networks for map completion. These map completion networks are primarily trained in the framework of generative adversarial networks (GANs) to extract the global structure of large amounts of existing map data. We show in experiments that the proposed method can estimate the environment map 1.3 times better than the previous SLAM methods in the situation of partial observation. GRAPHICAL ABSTRACT