The paper presents an algorithm for constructing a local path for a vehicle with nonholonomic kinematics of an automobile type. A local path is a sequence of transitions in the graph of possible maneuvers that minimizes a given cost function. The graph is constructed by duplicating along the global path pre-calculated in a curvilinear coordinate system set of kinematically feasible motion primitives. The use of pre-computed motion primitives significantly reduces the time of graph construction. The weight of each maneuver – the edge of the transition graph – is calculated as a weighted sum of costs based on several criteria. The specified cost function minimizes maneuvering and maintains a safe distance to static obstacles. The information about obstacles is extracted from an occupancy grid map. Dijkstra‘s algorithm is used to search a path in the weighted directed graph. The algorithm was tested on a dataset containing real road scenes. Each scene represents a given global path and a static environment model where a safe local path must be found. Local path search is performed in real-time. Experiments have shown that safe local paths have been found in all scenes where it was physically possible. At the same time, the obtained local paths were on average only on 1:3% longer than the given global paths which demonstrate the high applicability of the proposed algorithm.
The paper presents an algorithm for road markings detection in the image. The road markings are approximated by polyline with a restricted maximum curvature angle. To detect a marking segments an image is processed by a sliding window and for each window position, a straight line is detected by calculating Fast Hough Transform (FHT). Further, detected segments are grouped by relative position. Segments groups are then approximated by polylines. The algorithm was tested on real data collected from the front-looking camera of the autonomous vehicle driving at the experimental area “Kalibr” (Moscow). The road marking dataset used to evaluate the algorithm is publicly available at ftp://vis.iitp.ru/road markup dataset/. The precision of road markings detector was evaluated as 43%, and the recall as 73% which is sufficient for the autonomous vehicle precise positioning as demonstrated in [1].
This paper proposes a RANSAC-based algorithm for determining the axial rotation angle of an object from a pair of its tomographic projections. An equation is derived for calculating the rotation angle using one correct keypoints correspondence of two tomographic projections. The proposed algorithm consists of the following steps: keypoints detection and matching, rotation angle estimation for each point correspondence, outliers filtering with the RANSAC algorithm, finally, calculation of the desired angle by minimizing the re-projection error from the remaining correspondences. To validate the proposed method an experimental comparison against methods based on analysis of the distribution of the angles computed from all correspondences is conducted.
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.
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