MOMA: Visual Mobile Marker Odometry

In this paper, we present a cooperative odometry scheme based on the detection of mobile markers in line with the idea of cooperative positioning for multiple robots [1]. To this end, we introduce a simple optimization scheme that realizes visual mobile marker odometry via accurate fixed marker-based camera positioning and analyse the characteristics of errors inherent to the method compared to classical fixed marker-based navigation and visual odometry. In addition, we provide a specific UAV-UGV configuration that allows for continuous movements of the UAV without doing stops and a minimal caterpillar-like configuration that works with one UGV alone. Finally, we present a real-world implementation and evaluation for the proposed UAV-UGV configuration

Generating Compact Geometric Track-Maps for Train Positioning Applications

In this paper, we present a method to generate compact geometric track-maps for train-borne localization applications. Therefore, we first give a brief overview on the purpose of track maps in train-positioning applications. It becomes apparent that there are hardly any adequate methods to generate suitable geometric track-maps. This is why we present a novel map generation procedure. It uses an optimization formulation to find the continuous sequence of track geometries that fits the available measurement data best. The optimization is initialized with the results from a localization filter developed in our previous work. The localization filter also provides the required information for shape identification and measurement association. The presented approach will be evaluated on simulated data as well as on real measurements

Subtleties of extrinsic calibration of cameras with non-overlapping fields of view

The calibration of the relative pose between rigidly connected cameras with non-overlapping fields of view (FOV) is a prerequisite for many applications. In this paper, we focus on the subtleties of experimental realization of such a calibration optimization method presented in [1]. We evaluate two strategies to adapt a given optimization process to find better local minima. The first strategy is the introduction of a quality measure for the image data used for calibration, which is based on the projection size of known planar calibration patterns on the image. We show, that introducing an additional weighting to the optimization objective chosen as a function of that quality measure improves calibration accuracy and increases robustness against noise. The second strategy to further improve accuracy is a careful data acquisition of pose pairs used for the calibration. We integrate the above strategies into different setups and demonstrate the improvement both in simulation and real-world experiment

Dynamic Visual Motion Estimation

Visual motion is the projection of scene movements on a visual sensor. It is a rich source of information for the analysis of a visual scene. Especially for dynamic vision systems the estimation of visual motion is important because it allows to deduce the motion of objects as well as the self-motion of the system relative to the environment. Therefore, visual motion serves as a basic information for navigation and exploration tasks, like obstacle avoidance, object tracking or visual scene decomposition into static and moving parts

Single pose camera calibration using a curved display screen

In this paper, a method for single pose camera calibration is presented. Dense point correspondences are obtained by displaying structured light in a non-flat display screen (i.e. a curved display screen) which then are used as an input in common calibration algorithms. Experimental results demonstrate that the depth information present in a common commercial curved monitor with a radius of curvature of 1800 millimeters is sufficient in order to obtain calibration results comparable to the standard checkerboard method. In contrast to the commonly used checkerboard based calibration methods, the proposed method does not require to move the camera and it is cheaper and easier to implement than other methods based on expensive 3D calibration rigs

Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints

Train-borne localization systems as a key component of future signalling systems are expected to offer huge economic and operational advances for the railway transportation sector. However, the reliable provision of a track-selective and constantly available location information is still unsolved and prevents the introduction of such systems so far. A contribution to overcome this issue is presented here. We show a recursive multistage filtering approach with an increased cross-track positioning accuracy, which is decisive to ensure track-selectivity. This is achieved by exploiting track-geometry constraints known in advance, as there are strict rules for the construction of railway tracks. Additionally, compact geometric track-maps can be extracted during the filtering process which are beneficial for existing train localization approaches. The filter was derived applying approximate Bayesian inference. The geometry constraints are directly incorporated in the filter design, utilizing an interacting multiple model (IMM) filter and extended Kalman filters (EKF). Throughout simulations the performance of the filter is analyzed and discussed thereafter