Yixi Cai
Papers
12
Total Citations
405
H-Index
9
About
Yixi Cai is a robotics researcher whose work sits at the intersection of autonomous aerial systems, spatial data structures, and LiDAR-based perception and navigation. His research has made notable contributions to the foundational infrastructure enabling agile, safe, and intelligent unmanned aerial vehicles (UAVs). His most influential work, the ikd-Tree (2021, 68 citations), introduced a highly efficient incremental k-d tree data structure that dramatically reduces computation time for dynamic point cloud processing — a cornerstone advancement for real-time robotic systems. Building on this, Cai has developed innovative UAV platforms capable of avoiding small dynamic obstacles, extending sensor fields of view through self-rotation, and achieving high-speed autonomous navigation in unknown environments. His contributions to simulation and benchmarking are equally significant, with MARSIM and the MARS-LVIG dataset providing the community with critical tools for developing and evaluating LiDAR-visual-inertial systems. His occupancy mapping frameworks, D-Map and ROG-Map, further advance motion planning efficiency for high-resolution LiDAR sensors. With over 400 cumulative citations across a focused body of work, Cai has rapidly established himself as an impactful voice shaping the future of autonomous aerial robotics.
Research Focus
Key Achievements
Top Papers
- 1ikd-Tree: An Incremental K-D Tree for Robotic Applications68 citations · 2021
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- 4MARSIM: A Light-Weight Point-Realistic Simulator for LiDAR-Based UAVs55 citations · 2023
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- 6Occupancy Grid Mapping Without Ray-Casting for High-Resolution LiDAR Sensors29 citations · 2023
- 7Safety-assured high-speed navigation for MAVs27 citations · 2025
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