Lin Huican

About

Lin Huican is a robotics researcher whose work centers on simultaneous localization and mapping (SLAM), multi-robot formation control, and autonomous navigation. His most significant contribution is the development of ORB-SLAM-based systems for real-time robot tracking and dense 3D reconstruction using low-cost sensors like Kinect 2.0, a paper that has garnered 29 citations and demonstrated how sparse visual SLAM can be extended for practical robotic applications. He has also made notable advances in multi-robot coordination, proposing a state-switching formation control strategy using ultra-wideband (UWB) distance measurement (14 citations) and building a centralized-distributed multi-robot research platform on ROS with custom UWB ranging modules (10 citations). In reinforcement learning, Huican introduced a model-free, mapless navigation method using Q-learning that enables collision-free end-to-end control from lidar data (7 citations). His work bridges theoretical SLAM algorithms with real-world deployment, emphasizing cost-effective sensor integration and scalable multi-robot systems—achievements that have established him as a practical innovator in mobile robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   ORB-SLAM-based tracing and 3D reconstruction for robot using Kinect 2.0

   29 citations · 2017
2. 2
   Formation control of multi robot based on UWB distance measurement

   14 citations · 2018
3. 3
   Design and implementation of multi robot research platform based on UWB

   10 citations · 2017
4. 4
   A model-free mapless navigation method for mobile robot using reinforcement learning

   7 citations · 2018
5. 5
   A kinectV2-based 2D Indoor SLAM Method

   3 citations · 2017
6. 6
   Trajectory tracking control for heterogeneous mobile robots based on UWB ranging

   3 citations · 2018

Key Collaborators

Contact & Links