Student Projects

Available Projects

The optimal integration of simultaneously acquired datasets of the 2D domain (imagery) and the 3D domain (lidar point-cloud) is a direct task when geometric constraints are considered. However, in the absence of system calibrations, the fusion of the two optical datasets becomes uncertain and often fails to meet user expectations. Deep Neural Networks have managed to establish a spatial relationship between the 2D and the 3D domain, but current architectures have not yet been evaluated on long-range (< 200 m) aerial datasets.

Vegetation distribution in the Alps is directly related to geomorphic processes, water availability, plant dispersal modes (e.g. animals, wind) and indirectly to human activity from agriculture to leisure, and tourism. Nevertheless, recent warming trends have begun to affect the limits and spatial structure of vegetation colonies in the Alps, thereby threatening existing ecosystems in the upper altitudes. While these changes can be monitored locally, a region-wide characterisation is needed to accurately model and forecast potential change scenarios. To address this need, broad scale species distributions are required, accurately linking in-situ observations with Earth observation (EO) data.

This project utilizes deep learning techniques, specifically the Transformer architecture, to improve the accuracy of LiDAR point clouds in remote sensing tasks. By establishing 3D correspondences, the project aims to refine the point cloud accuracy and enable more precise terrain mapping, object recognition, change detection, and environmental monitoring.
The objectives include analyzing state-of-the-art techniques, implementing Transformer-based algorithms, and evaluating their effectiveness on LiDAR point cloud datasets. Challenges like noisy data, occlusions, and computational efficiency will be addressed through data preprocessing, leveraging the Transformer architecture’s self-attention mechanism.

Example of raw and processed LiDAR data usable as input to Transformer models

VDM-based navigation is a novel approach to autonomous navigation that improves the estimation of the navigation states (position, velocity, attitude) under normal and GNSS denied flight condition without the addition of extra-sensors. A custom delta-wing UAV has been constructed at TOPO with the objective of implementing real-time VDM-based navigation on an onboard computer.

The project’s goal is the development of the C++ based code based on Kalman Filtering with the platform aerodynamic model employed as process model. This will require the transfer of information logged in the autopilot (Pixhawk running PX4 firmware) to the companion computer using a RTPS/DDS bridge (PX4-ROS2 interface). This interface allows reliable sharing of time-critical/realtime information between the flight controller and offboard components.