Area Sensor Region Control for Spatial Position Tracking
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Solution Overview
Problem
Current laser tracker systems face limitations in achieving fast and reliable determination of spatial orientation due to the bottleneck of image data processing speed, leading to latency and potential errors in continuous tracking and position determination of auxiliary measuring instruments.
Innovation Solution
The method involves continuous camera image capture and selective pixel readout based on exposure values, defining areas of interest on the sensor to process only relevant pixels, and using a Kalman filter to anticipate and adjust image positions, thereby reducing data processing time and increasing repetition rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all pixels of the area sensor are read out and processed to determine image positions, then measurement precision is improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent divides the area sensor into multiple regions of interest (ROIs), each dedicated to detecting specific auxiliary point markers. Instead of processing all pixels across the entire sensor, the system segments the sensor area and processes only the relevant regions where markers are expected to appear, significantly reducing computational load while maintaining detection accuracy.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the sensor. Regions containing auxiliary point markers receive full processing attention with precise centroid calculations, while regions without markers are either skipped or processed with minimal effort. This local quality approach ensures high measurement precision where needed without sacrificing overall processing speed.
2Reliability
If the entire area sensor image is processed for each frame, then reliability of marker detection is improved, but loss of time increases due to continuous full-image processing
Solution Approach 1:
The patent performs preliminary actions by predicting the expected positions of auxiliary point markers based on previous frame data and motion models. Regions of interest are pre-defined around these predicted positions before actual marker detection occurs. This preliminary positioning ensures that when markers appear, they are already within the processed regions, maintaining detection reliability while avoiding full-image processing.
Solution Approach 2:
The system uses feedback from previously detected marker positions and motion information to continuously update and adjust the regions of interest for the current frame. This feedback mechanism ensures that ROIs remain aligned with actual marker positions even as the measuring instrument moves, maintaining high detection reliability without requiring static or exhaustive processing areas.
3Productivity
If regions of interest are continuously adapted based on expected marker positions, then productivity is improved by reducing processed pixels, but device complexity increases due to additional control functionality
Solution Approach 1:
The patent replaces complex mechanical or manual region adjustment mechanisms with computational methods. Instead of physically reconfiguring sensor regions or using complex control systems, the invention uses software-based algorithms to calculate and adapt regions of interest based on marker position predictions and motion models. This substitution of computational logic for mechanical complexity achieves dynamic ROI adaptation with minimal additional system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly enhances the speed and accuracy of spatial position and orientation determination of auxiliary measuring instruments, reducing latency and improving tracking precision.
Implementation Method 1
camera images are continuously captured with a camera having an area sensor with a multitude of pixels
Implementation Method 2
A target point can be represented by a retroreflective unit (e.g., a cube prism) that is targeted by an optical measuring beam from the measuring device, in particular a laser beam. The laser beam is reflected parallel back to the measuring device
Data Source
Figure 1
Figure 2~3
Figure 4a~4c
AI summary
Position determination method for continuously determining the spatial position of a measuring instrument (20) having at least one auxiliary point marker (22, 32, 32a, 32b) comprising a continuous acquisition of camera images (50a) of the at least one auxiliary point marker with a camera (12, 61) having an area sensor (30, 50) having a plurality of pixels (38a-d, 42), a continuous execution of readout cycles in which at least some of the pixels are read out with respect to a current exposure value, a continuous determination of at least one image position for the at least one depicted auxiliary point marker in the current camera image depending on exposure values obtained in the current readout cycle, and a continuous derivation of the current spatial position of the measuring instrument based on the at least one current image position, characterized in thatthat the determination of the at least one image position is carried out by the following steps: filtering pixels for which the exposure value obtained during readout fulfills a threshold criterion, in particular is above a threshold value; assembling (53) filtered pixels forming coherent row areas in a row (37a-d,41) of the area sensor into pixel disks (39a-d,51a); determining respective centroid components for each of the pixel disks; grouping the pixel disks such that pixel disks belonging to the at least one depicted auxiliary point marking are assigned to each other; and deriving the at least one image position of the at least one auxiliary point marking from the corresponding respective centroid components of the respective assigned pixel disks.