2 results about "Visual dictionary" patented technology

The invention relates to a line fault reason intelligent identification method based on a recording waveform image, and the method comprises the steps: carrying out the feature extraction of a fault waveform image through employing a scale invariant feature transform (SIFT) feature extraction method, and mining key feature information in the fault waveform image; a clustering method (K-means) is utilized to classify similar feature descriptors according to clusters to form a visual dictionary, and on this basis, an image pyramid technology is utilized to realize multi-scale feature fusion of image vectors; and a support vector machine (SVM) classifier is introduced, and vectorized image features are utilized to carry out accurate identification on fault causes. The line fault reason identification model constructed by the invention can effectively predict various power grid line fault reasons, and improves the accuracy and timeliness of fault reason identification; good robustness is shown in an actual case test, and generalization performance and stability of the model are ensured through cross validation and monitoring of model performance indexes; and powerful support is provided for fault diagnosis and maintenance of a power system.

This invention discloses a robot relocalization method, apparatus, and robot. In the method, laser point cloud data and image data acquired by the robot at its current position are received; keyframe information corresponding to the image data is determined according to a visual dictionary; physical location information corresponding to the keyframe information is obtained from a visual map; feature point information of the image data is matched with feature point information corresponding to the keyframe information; and calculation is performed by combining the physical location information of the feature points in the visual map and the pixel location information of the feature points in the image data. After obtaining the robot's first pose data, a predetermined area is set based on the first pose data, and iterative matching calculations are performed within the predetermined area using the laser map and laser point cloud data to obtain the robot's second pose data. By using the above scheme, the iteration range of laser matching can be reduced by utilizing the initial pose data, thus accelerating the matching speed and quickly achieving robot relocalization.