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Aerial Autonomy in Challenging Dynamic Environments
Automating drone navigation promises to revolutionise the way we conduct a wide variety of tasks, such as agricultural monitoring, industrial inspection, and disaster relief scenarios. Equipping a drone with the capability to autonomously explore and map previously unseen environments using onboard sensors and algorithms forms the basis of autonomy. While there has been tremendous progress in this area over the past few years [1-5], existing systems still lack reliability and adaptability to the challenges and complexity of real settings, which is crucial for the deployment of this technology in actual missions. In particular, performing robust navigation and mapping in highly dynamic environments (e.g., forests) remains an open challenge.
Following promising leads from the state-of-the-art and our in-house navigation stack, the goal of this project is to develop the capability to deal with increasingly dynamic and complex scenarios. The student will be guided towards leveraging the multi-sensor capabilities of a LiDAR-Visual-Inertial payload being developed in the lab to research approaches for perception and mission planning that can fuse information from the different sensors and capture high-fidelity representations of challenging dynamic environments. Initially, the student will work within a realistic simulation environment and then deploy and test their work onboard a real drone in a real setting.
Not specified
Literature review of relevant work and familiarisation with the existing codebase
Development of a new mapping/mission planning algorithm
Evaluation of the approach in simulation and on a real platform
Literature review of relevant work and familiarisation with the existing codebase Development of a new mapping/mission planning algorithm Evaluation of the approach in simulation and on a real platform
Experience with C++
Experience with ROS
Experience with C++ Experience with ROS
please send a email to border.rowan@ucy.ac.cy and lteixeira@mavt.ethz.ch
please send a email to border.rowan@ucy.ac.cy and lteixeira@mavt.ethz.ch