Visual-Inertial State Estimation
We develop probabilistic methods for high accuracy state estimation in unknown complex environments using measurements from cameras and IMUs. We focus on mathematical formulation for multi-sensor fusion, estimator initialization, sensor calibration, and robust motion estimation under aggressive motions.
Global Localization for Aerial Robots
We propose global localization methods for aerial robots in complex GPS-denied environments using publicly-available 3D models of cities and natural scenes. We develop both geometric- and deep learning-based methods for efficient global localization in city-scale environments.
Dense Mapping for Autonomous Navigation
We develop real-time methods for generating dense maps for large-scale autonomous navigation of aerial robots. We investigate into monocular and multi-camera dense mapping methods with special attention on the tight integration between maps and motion planning modules.
Trajectory Generation for Aerial Robots
We develop online methods to generate safe and smooth trajectories for aerial navigation through unknown, complex, and possibly dynamic environments. We use convex optimization tools to ensure both collision avoidance and dynamic feasibility.