Real-time dense monocular SLAM method and system based on online learning depth prediction network

Abstract

The invention discloses a real-time dense monocular simultaneous localization and mapping (SLAM) method based on an online learning depth prediction network. The method comprises: optimization of a luminosity error of a minimized high gradient point is carried out to obtain a camera attitude of a key frame and the depth of the high gradient point is predicted by using a trigonometric survey methodto obtain a semi-dense map of a current frame; an online training image pair is selected, on-line training and updating of a CNN network model are carried out by using a block-by-block stochastic gradient descent method, and depth prediction is carried out on the current frame of picture by using the trained CNN network model to obtain a dense map; depth scale regression is carried out based on the semi-dense map of the current frame and the predicted dense map to obtain an absolute scale factor of depth information of the current frame; and with an NCC score voting method, all pixel depth prediction values of the current frame are selected based on two kinds of projection results to obtain a predicted depth map, and Gaussian fusion is carried out on the predicted depth map to obtain a final depth map. In addition, the invention also provides a corresponding real-time dense monocular SLAM system based on an online learning depth prediction network.

Description

technical field [0001] The invention belongs to the technical field of computer vision three-dimensional reconstruction, and more specifically relates to a real-time dense monocular SLAM method and system based on an online learning depth prediction network. Background technique [0002] Simultaneous Localization And Mapping (SLAM) technology can predict the pose of the sensor in real time and reconstruct a 3D map of the surrounding environment, so it plays an important role in the fields of UAV obstacle avoidance and augmented reality. Among them, a SLAM system that only relies on a single camera as an input sensor is called a monocular SLAM system. Monocular SLAM has the characteristics of low power consumption, low hardware threshold and simple operation, and is widely used by researchers. However, the existing popular monocular SLAM systems, whether they are feature-based PTAM (Parallel Tracking And Mapping For Small AR Workspaces) and ORB-SLAM (Orb-slam: AVersatile And...

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Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method and system that combines online learning depth prediction network with monocular SLAM, the purpose of which is to make full use of the advantages of deep convolutional neural network to achieve Dense depth estimation of the key frames of the SLAM system, and restore the real scale information of the scene based on the results, thereby solving the technical problems of lack of scale information and inability to achieve dense mapping in traditional monocular SLAM

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