ORB FEATURE VISUAL ODOMETER LEARNING METHOD AND DEVICE BASED ON IMAGE SEQUENCE

摘要

Disclosed are an ORB feature visual odometer learning method and device based on an image sequence. The method comprises: recording images, and constituting an original data set by means of the plurality of obtained images; performing ORB feature extraction on the images in the original data set to realize extraction of first key features; performing feature extraction and matching on continuous images in the original data set by means of a convolutional neural network, and extracting rich second key features from the sequential images; and inputting the first key features and the second key features extracted from the original data set into a multi-layer long-short-term memory network for training and learning, and finally generating and outputting estimation of a visual odometer. According to the present application, feature extraction of ORB is composed of FAST features and Brief descriptors and is an initial preprocessing step, and rich first key features are extracted from an image sequence, and then a tracking algorithm is used for tracking the features in continuous frames, so that optical flow estimation from the image sequence is generated.