使用多个环境地图的全局结构从部分观测完成地图
Map completion from partial observation using the global structure of multiple environmental maps
摘要：
利用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建地图。自主移动机器人通常采用同时定位与地图构建（SLAM）方法来了解新访问环境中的可达区域。然而，传统的映射方法仅考虑传感器观测和控制信号来估计当前环境地图。本文提出了一种新的SLAM方法，基于地图补全网络的SLAM（MCN-SLAM），基于概率生成模型，结合深度神经网络地图补全。这些地图补全网络主要在生成对抗网络（GANs）的框架中进行训练，以提取大量现有地图数据的全局结构。实验表明，在部分观测的情况下，该方法可以比以前的SLAM方法更好地估计环境地图1.3倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
