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

A depth prediction and dense technology, applied in biological neural network models, image analysis, instruments, etc., can solve the problems of lack of scale information and inability to realize dense mapping in monocular SLAM, and achieve the effect of improving accuracy and accuracy
CN107945265AActive Publication Date: 2018-04-20HUAZHONG UNIV OF SCI & TECH
2 Cites 51 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUAZHONG UNIV OF SCI & TECH
Publication Date
2018-04-20

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

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.
Need to check novelty before this filing date? Find Prior Art

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More