25200?1495108884

陈谢沅澧 (学生)

Xieyuanli-Chen

国防科学技术大学

Ta在确实 5 个月

  • 湖南-长沙
  • 2017-04-18开始使用
  • 278次访问(自2016年5月)
Ta的动态
25200?1495108884
发布时间:2017-05-21 08:50
更新时间:2017-08-13 08:29

Gender: Male                                      Date of birth: Dec 03rd, 1992

Nationality: P. R. China                       Email: chenxieyuanli@hotmail.com

Phone: (+86)18711171605                 Zip code: 410073

Address: Room 601, Building 103, College of Mechatronic Engineering and Automation,

National University of Defense Technology, Changsha, China.


Research Interests


  • Rescue robots; Robot Localization; Multi-robot SLAM


Projects Experiences


  • Long-term visual SLAM in large-scale and outdoor unstructured environments, supported by the National Science Foundation of China. (09/2015-Present)
  • 2D/3D visual information fusion for bio-inspired SLAM, supported by the National Science Foundation of China. (09/2015-Present)
  • Rescue Robots, supported by the National University of Defense Technology. (09/2015-Present)
  • Task-oriented autonomous operation and control of rotor-flight manipulators, supported by the National Science Foundation of China. (07/2014-07/2015)


Education


  • Master in Robotics,National University of Defense Technology, Changsha, China (09/2015-Present) Supervisor: Prof. Hui Zhang.
  • Bachelor in Electrical Engineering and Automation,Hunan University, Changsha, China (09/2011-07/2015) Supervisor: Prof. Jianhao Tan and Yaonan Wang



Academic Activities                                    

  • Organizing Committee of RoboCup Rescue Robot League. (08/2017)
  • Technical Committee of Rescue Robot League in 2017 China Robot Contest. (08/2017)
  • Session Chair of the 2017 International Conference on Computer Vision Systems (ICVS). (07/2017)
  • Technical Committee of Rescue Robot League in 2017 RoboCup ChinaOpen. (04/2017)
  • Technical Committee of Rescue Robot League in 2016 China Robot Contest. (10/2016)


Publications

  • Xieyuanli Chen, Hui Zhang, Huimin Lu, Junhao Xiao, Qihang Qiu and Yi Li. Robust SLAM system based on monocular vision and LiDAR for robotic urban search and rescue[C], IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2017.

  • Xieyuanli Chen, Huimin Lu, Junhao Xiao, Hui Zhang, Pan Wang. Robust relocalization based on active loop closure for real-time monocular SLAM [C], The International Conference on Computer Vision Systems (ICVS). 2017
  • Yi Liu, Yuhua Zhong, Xieyuanli Chen, et al. The Design of a Fully Autonomous Robot System for Urban Search and Rescue[C], IEEE International Conference on Information and Automation (ICIA). 2016.
  • Yaonan Wang, Xieyuanli Chen, Jianhao Tan, et al. Fuzzy radial basis function neural network PID control system for a quadrotor UAV based on particle swarm optimization[C], IEEE International Conference on Information and Automation (ICIA). 2015.
  • Jianhao Tan, Chu Wang, Yaonan Wang, Xieyuanli Chen, et al. Three-dimensional path planning based on ant colony algorithm with potential field For rotary-wing flying robot[C], IEEE International Conference on Information and Automation (ICIA). 2015.
  • Jianhao Tan, Yaonan Wang, Yuanyuan Wang, Chu Wang, Xieyuanli Chen, et al. The research progress of the rotary-wing flight robot[J]. Control Theory and Applications. 2015. 32(10):1278-1286. (In Chinese)


回复 ︿
25200?1495108884
指派给   未指派
发布时间: 2017-07-17 10:34
更新时间:2017-07-17 10:34

This video is about the experimental results of the following paper: Xieyuanli Chen, Hui Zhang, Huimin Lu, Junhao Xiao, Qihang Qiu and Yi Li. Robust SLAM system based on monocular vision and LiDAR for robotic urban search and rescue.


Abstract. In this paper, we propose a monocular SLAM system for robotic urban search and rescue (USAR). Based on it, most USAR tasks (e.g. localization, mapping, exploration and object recognition) can be fulfilled by rescue robots with only a single camera. The proposed system can be a promising basis to implement fully autonomous rescue robots. However, the feature-based map built by the monocular SLAM is difficult for the operator to understand and use. We therefore combine the monocular SLAM with a 2D LiDAR SLAM to realize a 2D mapping and 6D localization SLAM system which can not only obtain a real scale of the environment and make the map more friendly to users, but also solve the problem that the robot pose cannot be tracked by the 2D LiDAR SLAM when the robot climbing stairs and ramps. We test our system using a real rescue robot in simulated disaster environments. The experimental results show that good performance can be achieved using the proposed system in the USAR. The system has also been successfully applied in the RoboCup Rescue Robot League (RRL) competitions, where our rescue robot team entered the top 5 and won the Best in Class Small Robot Mobility in 2016 RoboCup RRL Leipzig Germany, and the champions of 2016 and 2017 RoboCup China Open RRL.


回复 ︿
0?1470885445
登录后可添加回复
25200?1495108884
指派给   未指派
发布时间: 2017-07-17 10:26
更新时间:2017-07-17 10:27

This video is about the experimental results of the following paper: Xieyuanli Chen, Huimin Lu, Junhao Xiao, Hui Zhang, Pan Wang. Robust relocalization based on active loop closure for real-time monocular SLAM. Proceedings of the 11th International Conference on Computer Vision Systems (ICVS), 2017.


Abstract. Remarkable performance has been achieved using the state-of-the-art monocular Simultaneous Localization and Mapping (SLAM) algorithms. However, tracking failure is still a challenging problem during the monocular SLAM process, and it seems to be even inevitable when carrying out long-term SLAM in large-scale environments. In this paper, we propose an active loop closure based relocalization system, which enables the monocular SLAM to detect and recover from tracking failures automatically even in previously unvisited areas where no keyframe exists. We test our system by extensive experiments including using the most popular KITTI dataset, and our own dataset acquired by a hand-held camera in outdoor large-scale and indoor small-scale real-world environments where man-made shakes and interruptions were added. The experimental results show that the least recovery time (within 5ms) and the longest success distance (up to 46m) were achieved comparing to other relocalization systems. Furthermore, our system is more robust than others, as it can be used in different kinds of situations, i.e., tracking failures caused by the blur, sudden motion and occlusion. Besides robots or autonomous vehicles, our system can also be employed in other applications, like mobile phones, drones, etc.

回复 ︿
0?1470885445
登录后可添加回复
25200?1495108884
创建时间:2017-06-30 19:13
问题和建议
还能输入50个字符 提交

加入QQ群

关注微信APP


×