©2019 Toronto Robotics and AI Laboratory

Drone Landing

Visual Measurement Integrity Monitoring for UAV Localization

Chengyao Li, and Steven L. Waslander. 

[SSRR 2019, to appear]

A novel approach inspired by RAIM to monitor the integrity of visual localization for UAV. The algorithm first detects and rejects potential measurement outliers using a statistical approach, and calculates the protection level of the position estimation.

Towards Robust Autonomous MAV Landing with a Gimbal Camera

Christopher Choi and Steven L. Waslander. 

A more robust autonomous landing system for MAVs using a gimbal camera with complete tracking and landing modules. Results are present for both simulation and real MAVs. 

Thesis

Video

Autonomous Maritime Landings for Low-cost VTOL Aerial Vehicles

Kevin Ling, Derek Chow, Arun Das, and Steven L. Waslander. [CRV 2014]

An autonomous landing system of quadrotor UAV on a maritime vessel using an architecture that avoids sensor limitations while allowing for accurate relative pose estimation, even in the presence of wind disturbances. 

Paper

Video