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 generation of new useful map points and localization maintenance. In our experiments, we demonstrate the effectiveness of our measures using a low-cost quadrocopter. The proposed system only requires a single passive camera as exteroceptive sensor. Due to its explorative nature, the system achieves autonomous way-point navigation in challenging, unknown, GPS-denied environments.

The benefit of accurate camera calibration for recovering 3D structure from images is a well-studied topic. Recently 3D vision tools for end-user applications have become popular among large audiences, mostly unskilled in computer vision. This motivates the need for a flexible and user-centric camera calibration method which drastically releases the critical requirements on the calibration target and ensures that low-quality or faulty images provided by end users do not degrade the overall calibration and in effect the resulting 3D model. In this paper we present and advocate an approach to camera cal-ibration using fiducial markers, aiming at the accuracy of target calibration techniques without the requirement for a precise calibration pattern, to ease the calibration effort for the end-user. An extensive set of experiments with real images is presented which demonstrates improvements in the estimation of the parameters of the camera model as well as accuracy in the multi-view stereo reconstruction of large scale scenes. Pixel re-projection errors and ground truth errors obtained by our method are significantly lower compared to popular calibration routines, even though paper-printable and easy-to-use targets are employed. 1

Generating accurate 3D models for man-made environments can be a challenging task due to the presence of texture-less objects or wiry structures. Since traditional point-based 3D reconstruction approaches may fail to integrate these structures into the resulting point cloud, a different feature representation is necessary. We present a novel approach which uses point features for camera estimation and additional line segments for 3D reconstruction. To avoid appearance-based line matching, we use purely geometric constraints for hypothesis generation and verification. Therefore, the proposed method is able to reconstruct both wiry structures as well as solid objects. The algorithm is designed to generate incremental results using online Structure-from-Motion and linebased 3D modelling in parallel. We show that the proposed method outperforms previous descriptor-less line matching approaches in terms of run-time while delivering accurate

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 navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.

The quality and completeness of 3D models obtained by Structure-fromMotion (SfM) heavily depend on the image acquisition process. If the user gets feedback about the reconstruction quality already during the acquisition, he can optimize this process. The goal of this paper is to support a user during image acquisition by giving online feedback of the current reconstruction quality. We propose an online SfM method that integrates wide-baseline still-images in an online fashion into a consistent reconstruction and we derive a surface model given the SfM point cloud. To guide the user to scene parts that are captured not very well, we colour the mesh according to redundancy and resolution information. In the experiments, we show that our approach makes the final SfM result predictable already during image acquisition. The method is suited for large-scale reconstructions as obtained by flying micro aerial vehicles as well as on small indoor environments. We propose a method that supports a user in the acquisition process in two ways: (a) sparse online SfM with accuracy close to offline methods and (b) surface extraction and quality visualization. The workflow of our method is shown in Figure 1.

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, which gives the user the opportunity to interact. We demonstrate the performance of our approach by capturing several indoor and outdoor scenes on-the-fly and by evaluating our results with respect to a ground-truth model.

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 are automatically aligned to the DSM to allow the integration of highly detailed models into the original DSM and to provide geographic context.

Summary form only given. We report on a disruption in organizational dynamics arising from the introduction of model-driven development tools in General Motors. The introduction altered the balance of collaboration deeply, and the organization is still negotiating with its aftermath. Our report illustrates one consequence of tool adoption in groups, and that these consequences should be understood to facilitate technical change.

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 concept of virtual views in 3D space. Under the assumption that significant parts of the scene do not alter their geometry and serve as natural landmarks, the accuracy of our visual approach outperforms consumer grade GPS systems. In an experimental setup we compare our approach to a state-of-the-art visual SLAM algorithm and evaluate the performance by geometric validation from an observer's view. As our method directly allows global registration, it is neither prone to drift nor bias. This makes it well suited for long-term autonomous navigation.