The proposed algorithm matches local coordinates of each image with coordinates of the map by extracting roads’ line segments from the image and finding geometric transformation successfully matching the roads’ segments and the map’s ones. Parameters estimation is based on RANSAC algorithm which analyses the segments’ location and orientation.
In this paper, we present the precise indoor positioning system for mobile robot pose estimation based on visual edge detection. The set of onboard motion sensors (i.e. wheel speed sensor and yaw rate sensor) is used for pose prediction. A schematic plan of the building, stored as a multichannel raster image, is used as a prior information. The pose likelihood estimation is performed via matching of edges, detected on the optical image, against the map. Therefore, the proposed method does not require any deliberate building infrastructure changes and makes use of the inherent features of manmade structures - edges between walls and floor. The particle filter algorithm is applied in order to integrate heterogeneous localization data (i.e. motion sensors and detected visual features). Since particle filter uses probabilistic sensor models for state estimation, the precise measurement noise modeling is key to positioning quality enhancement. The probabilistic noise model of the edge detector, combining geometrical detection noise and false positive edge detection noise, is proposed in this work. Developed localization system was experimentally evaluated on the car-like mobile robot in the challenging environment. Experimental results demonstrate that the proposed localization system is able to estimate the robot pose with a mean error not exceeding 0.1 m on each of 100 test runs.
In this paper we study the problem of combining UAV obtained optical data and a coastal vector map in absence of satellite navigation data. The method is based on presenting the territory as a set of segments produced by color-texture image segmentation. We then find such geometric transform which gives the best match between these segments and land and water areas of the georeferenced vector map. We calculate transform consisting of an arbitrary shift relatively to the vector map and bound rotation and scaling. These parameters are estimated using the RANSAC algorithm which matches the segments contours and the contours of land and water areas of the vector map. To implement this matching we suggest computing shape descriptors robust to rotation and scaling. We performed numerical experiments demonstrating the practical applicability of the proposed method.
The classic way of aerial photographs geolocation is to bind their local coordinates to a geographic coordinate system using GPS and IMU data. At the same time the possibility of geolocation in a jammed navigation field is also of interest for practical purposes. In this paper we consider one approach to visual localization relatively to a vector road map without GPS. We suggest a geolocalization algorithm which detects image line segments and looks for a geometrical transformation which provides the best mapping between the obtained segments set and line segments in the road map. We consider IMU and altimeter data still known which allows to work with orthorectified images. The problem is hence reduced to a search for a transformation which contains an arbitrary shift and bounded rotation and scaling relatively to the vector map. These parameters are estimated using RANSAC by matching straight line segments from the image to vector map segments. We also investigate how the proposed algorithm’s stability is influenced by segment coordinates (two spatial and one angular).
This paper presents a method of radial distortion automatic compensation on video from an unknown camera. The proposed algorithm estimates the distortion parameters by analyzing a sequence of video frames. It does not require any calibration objects, but is based on the assumption that the original scene contained straight lines. The method tries to perform such radial distortion correction that makes lines look generally straighter. To estimate the overall curvature of the lines we propose to use the fast Hough transform; without actually detecting them in the image. The proposed algorithm has been tested on real data.
Demosaicing is the process of reconstruction of a full-color image from Bayer mosaic, which is used in digital cameras for image formation. This problem is usually considered as an interpolation problem. In this paper, we propose to consider the demosaicing problem as a problem of solving an underdetermined system of algebraic equations using regularization methods. We consider regularization with standard l<sub>1/2</sub>-, l<sub>1</sub> -, l<sub>2</sub>- norms and their effect on quality image reconstruction. The experimental results showed that the proposed technique can both be used in existing methods and become the base for new ones