Can one find his absolute GPS cordinates and view pose only using a single RGB camera in less than a second? Can you tell exactly where and from what angle a photo or video has been taken? In this project the students will join research team, developing machine learning solutions answering those challenges.
Can we teach our drones to ‘feel’ the aerodynamics under their wings? Is it possible to navigate and find one’s position only using your sensation of aerodynamics? Can we draw inspiration how nature has solved the challenge? In this project one will join an exiting research project investigating the vechicle dynamic navigation concept. State of the art wind tunnel testing, Computational Fluid Dynamics and Physics guided machine learning.
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 ecosystems and species that currently exist in these zones. While these changes can be monitored locally, region-wide characterisations are needed to accurately model and forecast them. To address this need broad scale species distributions are required, accurately linking ground based observations with Earth observation (EO) data.
The goal of this project is to develop deep learning methods that use multi-modal remote sensing technology (Images, LiDAR) to automatically detect individual tree species across broad scales. Such capacity will support future investigations that have the promise to reveal substantial insights on the evolution of tree colonisation patterns in the Alps and its relationship to the accelerated processes being observed as a result of climate change.
The combination of Global Navigation Satellite System (GNSS) with Inertial Navigation System (INS) has been widely used in order to improve the accuracy of a navigation solution for many transport systems. However, for safety-of-life applications and for future autonomous cars and UAVs, there are other important requirements in terms of integrity, availability or continuity. This task has been already addressed for civil aviation by integrity based systems like SBAS, GBAS or RAIM
This project is a collaboration between TOPO laboratory and Fastree3D, an EPFL/TU-Delft spin-off, based both in Lausanne and Delft. It leverages more than 20 years of academic research in CMOS image sensors and Single-Photon detectors. They focus on intelligent 3D vision systems for the automotive market. Their product is a flash LiDAR (or else called Time-Of-Flight camera, or range camera) that measures distances for each pixel based on time-of-flight technology. This project focuses on (1) defining the spatial resolution of the Fastree3D LiDAR: Ground Sampling Distance (GSD) and working distance (distance at which the output of the sensor is meaningful) and (2) creating a controlled calibration field and define the limits of the Fastree3D LiDAR.
Within this project, the student will implement a system to geo-reference (i.e., assign precise geographic coordinates on a map) to objects, such as vehicles, cars, buildings detected in 360 degree street-view images following an approach recently proposed in [1]. This has been successfully employed to automatically build a catalog for all the trees in a city from Google Street-View images. The student will learn the methods employed from the original authors and attempt to reproduce the results targeting specific classes of objects and a unique dataset in Africa
Terrestrial mobile mapping has been gaining popularity in the recent years. One of the greatest challenge in mapping is to know the exact location of the vehicle when pictures are taken. However, the buildings and vegetation in the area can cause situations where the GNSS signal is denied. This leads to drifts in the estimated position. In this project you will be exploring the introduction of a new measurement using the dynamics model of the vehicle in order to reduce this drift.
The use of redundant MEMS grade IMUs (R-IMU) present an economically and ergonomically viable solution to improve navigation performance in a classical sensor-fusion. However, such a system has never been tested in an aerodynamically constrained sensor fusion framework. Thanks to recent in-house development of R-IMU board (dahu4Nav), there is a hope to come up with a first ever sensor fusion architecture that has inertial redundancy in addition to already incorporated aerodynamics. However, there are numerous challenges on hardware, firmware and software fronts that need to be resolved.
The GNSS receiver is normally placed vertically above a point of interest and the measurements are reported to this point by knowing the height of the receiver’s antenna. The new receivers employes sensors which can measure the tilt (from the horizontal plane). Knowing the receiver’s tilt and slant distance to the point allows to correct a situation when the antenna is not centered perfectly above the point. The project goal lies in the determination of the accuracy of such tilt-compensation implemented by the receiver. The student shall propose the methodology and employ additional sensor(s)/observation to provide reference values. The results are then used and crosschecked with known points of interest already established around and outside EPFL.
Real-time navigation of small Uninhabited Aerial Vehicles (UAVs) is predominantly based on sensor fusion algorithms. Theses algorithms make use of an Extended Kalman Filter (EKF), wherein, state-dynamics, governed by dead-reckoning, are fused with GPS measurements to yield a navigation solution (position, velocity and attitude). However, during a GPS outage (1-2 minutes), this solution can drift around 1-2 Kilometers, adversely affecting safety and reliability of the mission. Recently, Vehicle Dynamic Model (VDM) based navigation system has shown significant improvement in positioning accuracy during a GPS outage. This is accomplished by incorporation of aerodynamics in the sensor fusion architecture. However, the modeled aerodynamics is old and there is a scope of improvement, thanks to availability of i) state-of-the-art quality flight data acquired in the vicinity of Lausanne and ii) wind tunnel experimentation at Laboratory of Intelligent System. The goal of this student project is to combine the data coming from real-flight campaign and wind tunnel experiments to come up with a new aerodynamic model of the in-house UAV and its integration in the sensor fusion framework.
In this semester project, students in the field of robotics or drone hobbyists will have the exciting opportunity to mechanize one/two existing Delta wing drones, enhancing their capabilities by installing a high-end GPS receiver, a new open-source autopilot system with custom features, implementing necessary interfacing while preparing mechanical attachment for an embedded computer.