Papers
14
Total Citations
262
H-Index
9
About
Kun Cao is a robotics researcher whose work sits at the intersection of perception, planning, and control for autonomous systems. His key research areas include LiDAR-inertial odometry, multi-robot coordination, and hybrid aerial-ground robotics. Cao’s most impactful contribution is **HCTO**, an optimality-aware LiDAR inertial odometry system that uses hybrid continuous-time optimization for compact wearable mapping (52 citations). He also developed **NEPTUNE**, a pioneering approach to entanglement-free trajectory planning for multiple tethered unmanned vehicles (39 citations), solving a critical problem in cable-driven robotics. Cao contributed to **PyPose**, a widely-used library that bridges deep learning and physics-based optimization for robot learning (35 citations), and designed **DoubleBee**, a novel hybrid aerial-ground robot with two active wheels that combines flight efficiency with ground mobility (28 citations). His work on uncertainty-aware model predictive control for motorized LiDAR systems (UA-MPC, 17 citations) advances 3D sensing for photogrammetry and BIM applications. With over 240 total citations across his portfolio, Cao’s innovations in multi-robot sweep coverage, relative docking, and topological path planning demonstrate a consistent focus on enabling robust, real-world autonomy for complex robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3PyPose: A Library for Robot Learning with Physics-based Optimization35 citations · 2023
- 4DoubleBee: A Hybrid Aerial-Ground Robot with Two Active Wheels28 citations · 2023
- 5Relative Docking and Formation Control via Range and Odometry Measurements25 citations · 2019
- 6
- 7Path Planning for Multiple Tethered Robots Using Topological Braids15 citations · 2023
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