Fu Zhang

About

Fu Zhang is a prominent robotics researcher whose work centers on autonomous navigation, sensor fusion, and simultaneous localization and mapping (SLAM). He is best known for pioneering tightly-coupled multi-sensor odometry systems that integrate LiDAR, inertial measurement units, and visual data to achieve robust, real-time state estimation. His FAST-LIVO and FAST-LIVO2 frameworks (195 and 106 citations respectively) have become influential benchmarks in the field, demonstrating how sparse-direct methods and iterated Kalman filtering can deliver both speed and accuracy in challenging environments. His Point-LIO system (143 citations) pushed the boundaries further by enabling high-bandwidth odometry capable of tracking extremely aggressive robotic motions. Beyond odometry, Zhang has made significant contributions to LiDAR hardware design, proposing retina-like scanning architectures (108 citations) that reduce costs without sacrificing performance. His ikd-Tree data structure (68 citations) addresses a critical computational bottleneck in dynamic mapping, while his work on autonomous UAVs extends these capabilities to aerial platforms capable of avoiding small dynamic obstacles. With over 950 cumulative citations across ten papers, Zhang's research has meaningfully advanced the practical deployment of intelligent, sensor-rich autonomous robots across both ground and aerial domains.

Research Focus

Key Achievements

Top Papers

1. 1
   FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry

   195 citations · 2022
2. 2
   Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry

   143 citations · 2023
3. 3
   Low-Cost Retina-Like Robotic Lidars Based on Incommensurable Scanning

   108 citations · 2021
4. 4
   FAST-LIVO2: Fast, Direct LiDAR–Inertial–Visual Odometry

   106 citations · 2024
5. 5
   Targetless Extrinsic Calibration of Multiple Small FoV LiDARs and Cameras Using Adaptive Voxelization

   83 citations · 2022
6. 6
   ikd-Tree: An Incremental K-D Tree for Robotic Applications

   68 citations · 2021
7. 7
   Avoiding Dynamic Small Obstacles With Onboard Sensing and Computation on Aerial Robots

   66 citations · 2021
8. 8
   A self-rotating, single-actuated UAV with extended sensor field of view for autonomous navigation

   66 citations · 2023
9. 9
   Moving event detection from LiDAR point streams

   60 citations · 2024
10. 10
    Efficient and Consistent Bundle Adjustment on Lidar Point Clouds

    59 citations · 2023

Key Collaborators

Contact & Links