In this project, we proposed methods for urban 3D reconstruction, which generate visually appealing (i.e., building models having planar surfaces and sharp edges, surroundings reconstructed as smooth surface) and compact reconstructions from urban environments using images acquired by Micro Aerial Vehicles (MAVs) as input. [more]
In this work, we present a robust edge-based visual odometry (REVO) system for RGBD sensors. Edges are more stable under varying lighting conditions than raw intensity values, which leads to higher accuracy and robustness in scenes, where feature- or photoconsistency-based approaches often fail. The results show that our method performs best in terms of trajectory accuracy for most of the sequences indicating that edges are suitable for a multitude of scenes.
RESIST addresses extreme events on critical structures, implemented in the case of bridges and tunnels attacked by all types of extreme physical, natural and man-made incidents (e.g. earthquakes). [More]
The Walkassist is a smart shoe developed to detect obstacles for visually impaired people using ultrasonic distance sensors. The aim of this research-project is to exploit additional visual information to support the ultrasonic based system. [More]
Precise models of the impact of explosions in urban environments provide novel and valuable information in disaster management for developing precautionary, preventive and mitigating measures. In this project, we develop methods enabling accurate predictions of the process and effect of detonations at particular locations in urban environments. [More]
The overall goal of the FWF-funded VMAV project is to advance the capabilities of small scale flying robots (UAV's) in the areas of flight behavior and flight autonomy. New algorithms will be investigated to make UAV's smarter, more agile and ultimately more useful for applications. [More]