Pose calculation method and system combining deep learning and geometric optimization

A technology of geometric optimization and deep learning, applied in the field of visual odometry in the field of computer vision, can solve problems such as weak interpretability and generalization ability, deviation of the true value of the result of pose estimation, ignoring the geometric constraints of dependencies, etc. Achieve the effect of strong interpretability and generalization ability, and improve the accuracy of pose estimation

Active Publication Date: 2020-05-12
ADVANCED INST OF INFORMATION TECH AIIT PEKING UNIV +1
View PDF5 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, this type of method can obtain excellent accuracy on the monocular depth estimation task with the help of binocular video training data, but it lags far behind the geometric method-based VO on the monocular pose estimation task.
The reason is that the interpretability and generalization ability of the neural network on the 3D geometric tasks that are strictly proved by mathematics is currently weak; this type of method can only estimate the pose change between two frames, ignoring the time sequence between multiple frames. Dependencies and geometric constraints on the local map, after a long period of error accumulation, the result of the pose estimation will deviate seriously from the true value

Method used

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
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Pose calculation method and system combining deep learning and geometric optimization
  • Pose calculation method and system combining deep learning and geometric optimization
  • Pose calculation method and system combining deep learning and geometric optimization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] Such as Figure 1-6 As shown, a pose calculation method and system combining deep learning and geometric optimization, including pose estimation module PoseNet and depth estimation module DepthNet, PoseNet module and Depth module need pre-training, pre-training uses existing models, for example, you can use In the pre-training model on KITTI odometry sequence 00-08, the PoseNet module inputs a binocular video sequence with a video frame length of 3, a video resolution of 1024×320, and outputs a 6DoF relative pose transformation, which is converted into =SE(3 ) (SE(3) is a special Euclidean group in Lie algebra); the DepthNet module inputs a single video frame and outputs a single channel depth map. The structure of PoseNet is a convolutional neural network structure. For example, a 7-layer convolutional layer is used. After each convolutional layer, an activation function is connected. The convolution kernel sizes are 7, 5, 3, 3, 3, 3, and 3 respectively. The structure...

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

PUM

No PUM Login to view more

Abstract

The invention discloses a pose calculation method combining deep learning and geometric optimization. The system comprises a pose estimation module PoseNet and a depth estimation module DepthNet, a pose estimation method based on deep learning and an optimization strategy based on geometric constraints are integrated into a visual odometer frame, luminosity errors, feature point reprojection errors, adjacent inter-frame constraints and constraints in a local map formed by a section of continuous frames are calculated, and real-time and accurate pose estimation can be carried out.

Description

technical field [0001] The invention belongs to the field of visual odometry in the field of computer vision, in particular to a pose calculation method and system combining deep learning and geometric optimization. Background technique [0002] Vision-based pose estimation (Visual Odometry, hereinafter referred to as VO) enables the robot to accurately locate in an unknown environment only relying on the data collected by the camera. In the past ten years, the VO framework based on the feature point method and the photometric error method has made great progress. The traditional VO based on the geometric method can perform very Lubang positioning in most cases, but in the absence of feature points or When the camera exposure is unstable, the accuracy of pose estimation will drop or even fail. In recent years, due to the development of deep learning and the explosive growth of data volume, learning-based VO has attracted more and more attention. It has two main advantages:...

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

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/73G06T7/50
CPCG06T7/73G06T7/50
Inventor 张兰清李宏
Owner ADVANCED INST OF INFORMATION TECH AIIT PEKING UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap