Camera Pose Estimation Using Neural Network and IMU Fusion
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Solution Overview
Problem
Current camera pose estimation techniques, such as Visual Odometry and Simultaneous Localization and Mapping, face significant computational challenges, particularly in real-time applications like vehicle navigation and augmented reality, where efficient and accurate estimation is crucial.
Innovation Solution
A system that combines inertial measurement unit data with neural network predictions to estimate camera pose by receiving image frames, generating neural network pose predictions, and adjusting previous camera poses using inertial data, then combining these predictions using a non-linear filter for improved accuracy and reduced computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Visual Odometry or SLAM techniques are used to estimate camera pose, then measurement precision is improved, but use of energy and computational requirements increase significantly
Solution Approach 1:
The patent combines IMU data with neural network predictions to estimate camera pose. The IMU provides motion compensation while the neural network processes image frames, merging two different sensing modalities (inertial and visual) to achieve accurate pose estimation with reduced computational load compared to traditional VO or SLAM methods
Solution Approach 2:
The neural network acts as an intermediary that processes image frames and generates pose predictions that are then adjusted by IMU data. This intermediary processing step enables the system to achieve accurate pose estimation without directly implementing computationally intensive traditional VO or SLAM algorithms
2Measurement precision
If traditional Visual Odometry or SLAM techniques are used to estimate camera pose, then measurement precision is improved, but productivity decreases due to computational complexity
Solution Approach 1:
The patent combines IMU data with neural network predictions to estimate camera pose. The IMU provides motion compensation while the neural network processes image frames, merging two different sensing modalities (inertial and visual) to achieve accurate pose estimation with reduced computational load compared to traditional VO or SLAM methods
Solution Approach 2:
The patent replaces the mechanical/computational system of traditional feature-based VO or SLAM with a neural network-based system. The neural network learns to estimate pose directly from image frames, substituting the complex mechanical processes of feature detection, matching, and spatial transformation with a learned model that runs more efficiently
3Measurement precision
If feature-based Visual Odometry is used to estimate camera pose, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces the mechanical/computational system of traditional feature-based VO or SLAM with a neural network-based system. The neural network learns to estimate pose directly from image frames, substituting the complex mechanical processes of feature detection, matching, and spatial transformation with a learned model that runs more efficiently
4Device complexity
If direct Visual Odometry is used to estimate camera pose, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent combines IMU data with neural network predictions to estimate camera pose. The IMU provides motion compensation while the neural network processes image frames, merging two different sensing modalities (inertial and visual) to achieve accurate pose estimation with reduced computational load compared to traditional VO or SLAM methods
Data Source
AI summary
A system for estimating a current camera pose corresponding to a current point in time using a previous camera pose corresponding to a previous point in time, of a camera configured to generate a sequence of image frames. The system performs operations, including: generating, using one or more neural networks, a neural network pose prediction for the current image frame; and adjusting a previous camera pose using inertial measurement unit data representing a motion of the camera between the previous point in time and the current point in time, to provide an inertial measurement unit pose prediction for the current point in time. The inertial measurement unit pose prediction, and the neural network pose prediction are combined in order to estimate the current camera pose.