Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify. Bayesian statistics offer a formalism to understand and quantify the uncertainty associated with deep neural network predictions. This tutorial provides an overview of the relevant literature and a complete toolset to design, implement, train, use and evaluate Bayesian Neural Networks, i.e. Stochastic Artificial Neural Networks trained using Bayesian methods.Comment: 35 pages, 15 figure

Accuracy Prediction in Aerial Mapping

Low cost and low weight unmanned aerial vehicle (UAV) systems with imaging capability have enjoyed a rapid development over the past years and are increasingly deployed as carriers for mapping purposes. They present a well-established tool for local-area remote sensing in the fields of agriculture, forestry, mining and hydrology as well as in scientific research. An important part of MAV mapping system is the ground station with a mission planner which serves for flight scheduling and mission execution. The traditional mission planners for MAVs are not dedicated to precise photogrammetry in complicated terrain. They allow planning and executing of autonomous flight as well as setting up of the autopilot systems. However, they lack functions for advanced flight planning, such as those motivated by achieving certain precision and reliability of the determined coordinates of features within the mapped area. The goal of this work is to create a software tool that given a planned trajectory (i.e. planned position and orientation of camera exposure), preliminary digital elevation model (DEM), assumptions on surface texture (i.e. number, distribution and accuracy of image observations) and (optionally) a certain number and distribution of ground control points (GCPs), allow to quantify the quality of the mapping

BRDF acquisition with polarizing filters (2016)

BRDF (for bidirectional reflectance distribution function) are functions used in computer graphics and optics to describe how an opaque surface reflect the incoming light. The goal of this project is to study technics to separate different components of the BRDF real objects, measured by a static camera with different incoming light direction. There’s different applications for such separation. The main one being es- timating parameters of approximation models used to reduce the space needed to store the BRDF, or reconstruct the full continious signal (sampled during acquisition). LCAV144505091

Mapping quality prediction for RTK/PPK-equipped micro-drones operating in complex natural environment

Drone mapping with GNSS-assisted photogrammetry is a highly efficient method for surveying small -or medium- sized areas. However, the mapping quality is not intuitively predictable, particularly in complex environments (with steep and cluttered terrain), in which the quality of the real-time kinematic (RTK) or postprocessed kinematic (PPK) positioning varies. We present a method to predict the mapping quality from the information that is available prior to the flight, such as the flight plan, expected flight time, approximate digital terrain model, prevailing surface texture, and embedded sensor characteristics. After detailing the important considerations, we also present the concept of global precision within the context of minimal and efficient ground control point placement in a complex terrain. Finally, we validate the proposed methodology by means of rigorous statistical testing against numerous experiments conducted under different mapping conditions