Spatial Motion Calculation Using Multi-Source Similarity Integration
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Solution Overview
Problem
Current technologies fail to accurately calculate spatial motion of moving objects by considering image positions, spatial positions, and feature descriptors simultaneously, especially when spatial positions contain noise, leading to unstable estimation.
Innovation Solution
A spatial motion calculation apparatus and method that inputs image and spatial positions, along with feature descriptors of feature points before and after an object's movement, using similarity functions to determine shifts and depth parameters, integrating these to calculate the object's spatial motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial positions are estimated using triangulation with multiple TV cameras, then three-dimensional spatial motion can be calculated, but the estimated spatial positions contain large amounts of noise leading to unstable spatial motion calculation
Solution Approach 1:
The patent combines three different types of information (image positions from 2D images, spatial positions from triangulation, and feature descriptors from image analysis) into a unified cost function. This integration allows the system to leverage the strengths of each data source while compensating for their individual weaknesses, particularly using the noise-resistant image positions and feature descriptors to stabilize the spatial motion calculation despite noisy spatial position estimates.
Solution Approach 2:
The patent transforms the spatial motion calculation problem into an optimization problem by defining a cost function with unknown parameters (image position shift and depth direction parameter). By adjusting these parameters to minimize the cost function, the system can derive stable spatial motion estimates that are robust to noise in the input spatial positions.
2Reliability
If only image positions and feature descriptors are used for tracking, then noise resistance is improved, but three-dimensional spatial motion cannot be accurately calculated
Solution Approach 1:
The patent merges 2D image tracking information with 3D spatial position information in a unified cost function. This allows the system to maintain the noise resistance of image-based tracking while incorporating the three-dimensional spatial information needed for accurate spatial motion calculation, resolving the contradiction between tracking stability and spatial accuracy.
3Measurement precision
If spatial positions contain large amounts of noise, then measurement capability is maintained, but spatial motion cannot be calculated in a stable manner
Solution Approach 1:
The patent acknowledges that spatial position measurements from triangulation inherently contain noise, but instead of trying to eliminate this noise source, it incorporates the noisy measurements into a cost function that is minimized through optimization. The optimization process effectively filters the noise by finding the parameter values that best explain all observations collectively, converting the harmful noise into a manageable statistical variation.
Data Source
AI summary
A spatial motion calculation apparatus includes an image position relation calculation unit that calculates first similarities based on an image position relation between an inputted groups of feature points 1 and 2, a spatial position relation calculation unit that calculates second similarities based on an spatial position relation between said inputted groups, a feature descriptor relation calculation unit that calculates third similarities based on a feature descriptor relation between said inputted groups, and a spatial motion calculation unit that estimates the spatial motion based on the result that integrates the first to third similarities.