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We introduce a novel approach for separating and segmenting individual facades from streetside images. Our algorithm incorporates prior knowledge about arbitrarily shaped repetitive regions which are detected using intensity profile descriptors and a voting–based matcher. In the experiments we compare our approach to extended state–of–the–art matching approaches using more than 600 challenging streetside...
The main contribution of this paper is to bridge the gap between passive monocular SLAM and autonomous robotic systems. While passive monocular SLAM strives to reconstruct the scene and determine the current camera pose for any given camera motion, not every camera motion is equally suited for these tasks. In this work we propose methods to evaluate the quality of camera motions with respect to the...
Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous...
Micro aerial vehicles (MAVs) are gaining importance as image acquisition tools in urban environments, where areas of interest are often close to buildings and to the ground. While GPS is still the most widely used sensor for outdoor localization, urban applications motivate the change towards visual localization. We present a framework based on metric, geo-referenced visual landmarks, which can be...
We present a novel system that is capable of generating live dense volumetric reconstructions based on input from a micro aerial vehicle. The distributed reconstruction pipeline is based on state-of-the-art approaches to visual SLAM and variational depth map fusion, and is designed to exploit the individual capabilities of the system components. Results are visualized in real-time on a tablet interface,...
We present an image-based 3D reconstruction pipeline for acquiring geo-referenced semi-dense 3D models. Multiple overlapping images captured from a micro aerial vehicle platform provide a highly redundant source for multi-view reconstructions. Publicly available geo-spatial information sources are used to obtain an approximation to a digital surface model (DSM). Models obtained by the semi-dense reconstruction...
We present a concept for automatic construction site monitoring by taking into account 4D information (3D over time), that is acquired from highly-overlapping digital aerial images. On the one hand today's maturity of flying micro aerial vehicles (MAVs) enables a low-cost and an efficient image acquisition of high-quality data that maps construction sites entirely from many varying viewpoints. On...
We present a novel technique for the automatic alignment of Structure from Motion (SfM) models, acquired at ground level or by micro aerial vehicles, to an overhead Digital Surface Model (DSM) using GPS information. An additional refinement step based on the correlation of the DSM height map with the model height map corrects for the GPS localization uncertainties and results in precisely aligned...
Highly accurate localization of a micro aerial vehicle (MAV) with respect to a scene is important for a wide range of applications, in particular surveillance and inspection. Most existing approaches to visual localization focus on indoor environments, while such tasks require outdoor navigation. Within this work, we introduce a novel algorithm for monocular visual localization for MAVs based on the...
We examine a new method of façade segmentation in a multi-view scenario. A set of overlapping, thus redundant street-side images exists and each image shows multiple buildings. A semantic segmentation identifies primary areas in the image such as sky, ground, vegetation, and façade. Subsequently, repeated patterns are detected in image segments previous labeled as "façade areas" and are...
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