Fanze Kong
Papers
13
Total Citations
577
H-Index
10
About
Fanze Kong is a leading roboticist whose research pushes the boundaries of autonomous aerial navigation, with a particular focus on high-speed, agile flight in cluttered and dynamic environments. His core contributions lie in advancing Light Detection and Ranging (LiDAR) inertial odometry and perception for Micro Air Vehicles (MAVs). Kong is best known for developing **Point-LIO** (143 citations), a groundbreaking LiDAR-inertial odometry that enables robust state estimation during extremely aggressive maneuvers. He also pioneered novel approaches for avoiding small, dynamic obstacles like tree branches and power lines (66 citations), and introduced a self-rotating, single-actuated UAV design that dramatically extends the sensor field of view for autonomous navigation (66 citations). His work on moving event detection from LiDAR point streams (60 citations) and large-scale consistent mapping using hierarchical LiDAR bundle adjustment (55 citations) further demonstrates his impact on the field. Kong also created **MARSIM**, a lightweight, point-realistic simulator for LiDAR-based UAVs (55 citations), and the **MARS-LVIG** multi-sensor SLAM dataset (41 citations), providing essential tools for the research community. His work consistently achieves high citation counts, reflecting its significance in enabling safe, high-speed, and fully autonomous flight for aerial robots.
Research Focus
Key Achievements
Top Papers
- 1Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry143 citations · 2023
- 2
- 3
- 4Moving event detection from LiDAR point streams60 citations · 2024
- 5
- 6MARSIM: A Light-Weight Point-Realistic Simulator for LiDAR-Based UAVs55 citations · 2023
- 7
- 8Occupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors29 citations · 2023
- 9Safety-assured high-speed navigation for MAVs27 citations · 2025
- 10