Fanfei Chen

About

Fanfei Chen is a roboticist and AI researcher whose work sits at the intersection of autonomous exploration, simultaneous localization and mapping (SLAM), and deep learning for mobile robotics. His research addresses one of the fundamental challenges in robotics: enabling robots to intelligently navigate and map unknown environments with minimal human intervention. Chen's early contributions introduced information-theoretic approaches to autonomous exploration, combining Bayesian optimization with mobile robot control to maximize information gain during navigation — work that has attracted over 130 citations. He subsequently pioneered the application of deep reinforcement and supervised learning to autonomous mapping, developing graph-based frameworks that allow robots to make real-time exploration decisions under localization uncertainty. His DiSCo-SLAM framework (112 citations) represents a landmark contribution to multi-robot LiDAR SLAM, enabling efficient distributed mapping through lightweight data exchange between robots. Chen has also extended his work to challenging underwater environments, developing sonar-based SLAM systems enhanced by overhead imagery and cluttered-environment exploration strategies for autonomous underwater vehicles. With a portfolio spanning simulation-based LiDAR super-resolution and zero-shot policy transfer, Chen's cumulative impact — exceeding 640 citations — reflects his broad influence on the future of autonomous robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Information-theoretic exploration with Bayesian optimization

   132 citations · 2016
2. 2
   DiSCo-SLAM: Distributed Scan Context-Enabled Multi-Robot LiDAR SLAM With Two-Stage Global-Local Graph Optimization

   112 citations · 2021
3. 3
   Simulation-based lidar super-resolution for ground vehicles

   83 citations · 2020
4. 4
   Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs

   74 citations · 2020
5. 5
   Toward autonomous mapping and exploration for mobile robots through deep supervised learning

   61 citations · 2017
6. 6
   Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning

   53 citations · 2019
7. 7
   Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

   41 citations · 2022
8. 8
   Information-Driven Path Planning

   34 citations · 2021
9. 9
   Overhead Image Factors for Underwater Sonar-Based SLAM

   34 citations · 2022
10. 10
    Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty

    18 citations · 2021

Key Collaborators

Contact & Links