Author name cluster

Guangfu Che

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

IROS Conference 2023 Conference Paper

An Open-Source Robotic Chinese Chess Player

Shan An
Guangfu Che
Jinghao Guo
Yanjie Xu
Guoxin Wang
Konstantinos A. Tsintotas
Fukai Zhang
Junjie Ye 0004

Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robot operating system, real-time and accurate chess recognition. Regarding its mechanical design, it combines a magnetism structure and mechanical cam drive, while the overall system has just three servo motors. At the same time, its control strategy is simple and effective. Furthermore, a lightweight robot message communication mechanism, entitled TinyROS, is developed for computing resource-limited embedded chips. Concerning the recognition process, our CNNbased object detector determines chess and achieves accurate identification. As a result, our robotic Chinese chess player is exquisite and easy for large-scale promotion while improving users' chess skills. Aiming to facilitate future consumer robot research and popularize customer robots, the model's mechanical and software design and the TinyROS protocol are open-sourced at https://github.com/Star-Robot/chinese-chess-robot.

Details

IROS Conference 2019 Conference Paper

Fast and Incremental Loop Closure Detection Using Proximity Graphs

Shan An
Guangfu Che
Fangru Zhou
Xianglong Liu 0001
Xin Ma
Yu Chen

Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently used bag-of-words (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time costs and fewer memory costs for mobile robot applications is not well satisfied. In this paper, we propose a novel loop closure detection framework titled FILD’ (Fast and Incremental Loop closure Detection), which focuses on an on-line and incremental graph vocabulary construction for fast loop closure detection. The global and local features of frames are extracted using the Convolutional Neural Networks (CNN) and SURF on the GPU, which guarantee extremely fast extraction speeds. The graph vocabulary construction is based on one type of proximity graph, named Hierarchical Navigable Small World (HNSW) graphs, which is modified to adapt to this specific application. In addition, this process is coupled with a novel strategy for real-time geometrical verification, which only keeps binary hash codes and significantly saves on memory usage. Extensive experiments on several publicly available datasets show that the proposed approach can achieve fairly good recall at 100% precision compared to other state-of-the-art methods. The source code can be downloaded at https://github.com/AnshanTJU/FILD for further studies.

Details