Student Projects

The goal of this project is to develop a lidar-visual-inertial odometry approach that integrates visual and lidar measurements into a single unified representation. The starting point, inspired by FAST-LIVO2 [1], will be to investigate methods for efficiently combining visual patches from camera images with a set of geometric primitives extracted from FAST-LIO2 [4], a lidar-inertial odometry pipeline. The performance of the resulting approach will be evaluated in comparison with existing lidar-visual-inertial odometry approaches. References: [1] C. Zheng et al., “FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry,” IEEE Transactions on Robotics, 2024. [2] J. Lin and F. Zhang, “R3LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. [3] T. Shan, B. Englot, C. Ratti, and D. Rus, “LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping,” in IEEE International Conference on Robotics and Automation, 2021. [4] W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang, “FAST-LIO2: Fast Direct LiDAR-inertial Odometry,” arXiv, 2021.

- WP1: Literature review of work on lidar-visual-inertial odometry. - WP2: Develop a lidar-visual-inertial odometry approach with a single unified representation. - WP3: Evaluate the performance of the approach in comparison with existing work.

Experience with C++ and ROS.

Please send CV and transcripts to Rowan Border (border.rowan@ucy.ac.cy) and Ruben Mascaro (rmascaro@ethz.ch).

The objective of the thesis is to develop an algorithm capable of real-time collision avoidance for drones, ensuring safe operation even in environments with other air traffic, such as airplanes, helicopters, paragliders, and similar obstacles. The tasks include selecting and evaluating existing neural networks and algorithms, or designing a custom solution tailored to our requirements. The project also involves working with a high-performance hardware setup, with support for real-time execution on an NVIDIA Jetson Orin. Avientus provides a flexible and dynamic research environment with a budget for necessary hardware, such as cameras and sensors. Students are encouraged to experiment with and choose tools, hardware, and software frameworks that best suit their approach. This thesis offers a unique opportunity to work on cutting-edge technology in a startup environment, contributing directly to the future of automated drone logistics. If you are passionate about robotics, computer vision, and real-time systems, and are excited to tackle challenges in the aviation domain, we’d love to hear from you!

- Strong competencies in computer vision & neural networks - Good knowledge in programming (Python and / or C++) - General interest in VTOL drones

Please send your CV and transcripts to info@avientus.ch and rmascaro@ethz.ch