摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于机器人的新报告。根据NewsRx记者从中华人民共和国上海发回的新闻报道，研究表明：“与几何地图相比，拓扑地图的存储空间较小，但在重新定位和路径规划能力方面受到限制。”本研究经费来源于国家自然科学基金(NSFC)。本报编辑引用上海交通大学的一篇研究文章：“为解决这一问题，提出了一种基于特征的混合拓扑地图(FHT-Map)，以及一种基于机器人实验的实时地图构建算法。”FHT-MAP利用RGB摄像机和LiDAR Informat Ion，由主节点和支持节点两种类型组成，主节点通过卷积神经网络压缩视觉信息和局部LAS ER扫描数据增强后续的重新定位能力，支持节点RETA以最少的数据量保证存储效率，同时便于路径规划。仿真结果表明,所提出的fht-map能够有效地提高机器人的再定位和路径规划能力。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ro botics. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “Topological maps are favo rable for their small storage compared to geometric maps. However, they are limi ted in relocalization and path planning capabilities.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Shanghai Jiao Tong Univ ersity, “To solve the problem, a feature-based hybrid topological map (FHT-Map) is proposed along with a real-time map construction algorithm based on robot exp loration. Specifically, the FHT-Map utilizes both RGB cameras and LiDAR informat ion and consists of two types of nodes: main node and support node. Main nodes s tore visual information compressed by convolutional neural network and local las er scan data to enhance subsequent relocalization capability. Support nodes reta in a minimal amount of data to ensure storage efficiency while facilitating path planning. After map construction through robot exploration, the FHT-Map can be used by other robots for relocalization and path planning. Simulation results de monstrate that the proposed FHT-Map can effectively improve relocalization and p ath planning capability compared with other topological maps.”
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