Student Projects

**Background** Visual SLAM (Simultaneous Localization and Mapping) enables robots to navigate unknown environments by building 3D maps using visual data from onboard cameras. While these maps are optimized for robotic perception and localization, they often lack the structural clarity and semantic richness of CAD models that humans rely on for spatial understanding. Additionally, they often differ in scale, completeness, and accuracy due to sensor noise and perception limitation. These mismatches create a communication gap between autonomous systems and human operators. **Goals** To bridge this gap, this thesis explores the problem of registering maps generated by SLAM with CAD floor plans. By establishing the projection between the robot-centric and human-centric representations, the approach will improve human-robot interaction, environment interpretation, and integration of autonomous mobile robots into existing workflows that rely on structured 2D CAD maps. Additionally, the thesis will explore how such alignment can support refining SLAM maps using CAD priors. **Proposed Method** The alignment will be formulated as a non-linear projection problem between the two map representations [1], while preserving local and global consistency of given correspondences. The problem can be solved using optimization techniques. As an optional continuation, the accurate geometric and semantic information in the CAD structure can be utilized to correct the distortion in the SLAM map. The approach will be evaluated on a real robot. The exact method remains open to refinement and innovation. It’s encouraged to explore alternative methods. **Expected outcome** ● A novel spatial registration algorithm to align SLAM-generated maps with architectural CAD models ● A quantitative and qualitative analysis of the proposed method.

● Strong self-motivation and curiosity for solving challenging robotic problems ● Previous experience in the fields of optimization, computer vision and VSLAM ● Excellent programming skills in C++ and Python ● Experience with Linux, ROS, and typical development tools such as git are advantageous ● An excellent academic record is desirable, but may be compensated by expert knowledge in the areas mentioned above

If you are interested, please send your transcripts and CV to Zimeng Jiang (zimeng.jiang@sevensense.ch) and Roxane Merat (roxane.merat@sevensense.ch).

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