Monocular Camera Pose Estimation via Neural Scale Adjustment
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Solution Overview
Problem
Monocular visual SLAM algorithms face scale ambiguity issues in accurately estimating camera pose changes between images, leading to inaccurate movement prediction despite accurate feature point matching.
Innovation Solution
A neural network is employed to estimate camera position changes between images captured at different times, which are then used to adjust the scale of initial pose variation estimates, thereby minimizing errors and resolving scale ambiguity in SLAM operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monocular visual SLAM algorithms perform feature point matching between images, then the initial pose variation estimation is obtained, but scale ambiguity arises leading to inaccurate camera movement prediction
Solution Approach 1:
The patent introduces an intermediary component (scale adjustment module) that mediates between the feature point matching results and the final pose estimation. This module uses the estimated position change from neural network as a reference to adjust the scale of the initial pose variation, resolving the scale ambiguity without requiring additional sensors or complex modifications to the existing SLAM pipeline.
Solution Approach 2:
The patent changes the scale parameter of the initial pose variation estimation by using the estimated position change from the neural network as a reference. This parameter adjustment resolves the scale ambiguity issue while maintaining the computational efficiency of monocular visual SLAM algorithms.
2Measurement precision
If neural network is used to estimate camera position change, then scale ambiguity is reduced, but computational complexity increases
Solution Approach 1:
The patent applies partial action by using the neural network only for estimating position change magnitude, while the existing feature point matching algorithm continues to handle pose variation estimation. This partial application of neural network reduces the overall computational burden compared to replacing the entire SLAM pipeline.
Solution Approach 2:
The patent segments the pose estimation task into two parts: (1) initial pose variation estimation through feature point matching, and (2) scale adjustment using neural network-based position change estimation. This segmentation allows each component to be optimized independently, balancing accuracy and computational complexity.
3Measurement precision
If scale adjustment is performed based on neural network estimation, then camera movement prediction accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by estimating the position change using the neural network before the scale adjustment step. This allows the scale adjustment to be performed efficiently using pre-computed position change information, reducing the overall processing time compared to iterative optimization approaches.
Data Source
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AI summary
Disclosed are a device and method with pose estimation. A method of operating an electronic device includes: obtaining monocular images captured at different respective points in time by a same camera; estimating, based on a neural network, a position change of the camera corresponding to the time between the points in time at which the images were captured; and estimating a position and/or orientation of the camera based on the estimated position change of the camera and based on the images.