Multi-Degree-of-Freedom Camera Pose Calibration via Deep Neural Network
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in navigation without GPS data, requiring efficient calibration methods for multi-degree-of-freedom (MDF) pose determination to ensure accurate localization and obstacle avoidance in environments with weak or absent satellite-based positioning signals.
Innovation Solution
A calibration device and method using a deep neural network, specifically a convolutional neural network (CNN), to determine the MDF pose of cameras affixed to a structure, which calculates a global MDF pose by combining camera and calibration device poses, enabling autonomous navigation even in GPS-denied areas through image data from RGBD cameras and sensors like lidar.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used for camera pose determination, then calibration accuracy can be achieved, but calibration time and system downtime increase significantly
Solution Approach 1:
The patent replaces manual mechanical calibration procedures with an automated deep learning-based system. The CNN model automatically processes images from multiple cameras to determine MDF pose, eliminating the need for manual calibration operations while maintaining high accuracy. This substitution of mechanical/manual processes with automated computational methods directly reduces calibration time and system downtime.
Solution Approach 2:
The system performs self-calibration through automated MDF pose determination using the deep learning model. The cameras and processing system automatically calculate poses and perform calibration without requiring external manual intervention, enabling the system to calibrate itself rapidly and reduce downtime while maintaining precision.
2Measurement precision
If traditional single-degree-of-freedom pose determination is used, then system complexity remains low, but navigation accuracy in GPS-denied environments is insufficient
Solution Approach 1:
The patent combines multiple cameras and integrates their data to determine MDF pose, merging multiple data sources and processing functions into a unified deep learning system. This integration of multiple cameras and processing steps into a single CNN-based workflow achieves high navigation accuracy while managing system complexity through unified processing.
Solution Approach 2:
The system transitions from traditional single-degree-of-freedom pose determination to multi-degree-of-freedom pose determination, adding dimensional complexity to capture full 3D spatial orientation (position and orientation). This dimensional expansion enables accurate navigation in GPS-denied environments by providing comprehensive pose information, with the deep learning system managing the increased complexity automatically.
3Productivity
If automated deep learning-based MDF pose determination is implemented, then calibration time is reduced and productivity increases, but computational complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images and preparing data before the main pose determination step. The CNN model is pre-trained and ready to process images rapidly, and the system pre-aligns multiple camera views before MDF pose calculation. These preliminary preparations enable faster automated calibration and higher productivity while managing computational complexity through organized data flow.
Data Source
AI summary
A calibration device and method of calculating a global multi-degree of freedom (MDF) pose of a camera affixed to a structure is disclosed. The method may comprise: determining, via a computer of a calibration device, a calibration device MDF pose with respect to a global coordinate system corresponding to the structure; receiving, from an image system including the camera, a camera MDF pose with respect to the calibration device, wherein a computer of the image system determines the camera MDF pose based on an image captured by the camera including at least a calibration board affixed to the calibration device; calculating the global MDF pose based on the calibration device MDF pose and the MDF pose; and transmitting the global MDF pose to the image system such that a computer of the image system can use the global MDF pose for calibration purposes.