Active Sensor Point Clouds for Blooming Artifact Detection
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Solution Overview
Problem
Existing active sensor systems face challenges in reliably distinguishing between blooming artifacts and actual objects, particularly with highly reflective objects, leading to unreliable identification and false-positive detections.
Innovation Solution
The method involves generating multiple point clouds at different measurement periods with varying sensitivities and comparing the spatial extent of high-energy regions to identify and mark potential blooming artifacts, thereby reducing the risk of false-positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If image subtraction method is used to reduce blooming effects, then blooming artifacts are reduced, but reliability of artifact identification deteriorates due to inability to distinguish between artifacts and actual objects
Solution Approach 1:
The patent applies dynamics by performing multiple measurements at different sensitivities and dynamically comparing the spatial extent of high-energy regions across measurements. The system adapts its detection criteria based on the temporal evolution of detected features, marking regions as artifacts only when their spatial extent changes between measurements, thereby reliably distinguishing artifacts from static actual objects.
Solution Approach 2:
The patent changes the sensitivity parameter of the sensor system between measurements. By capturing point clouds at different sensitivity levels and comparing the spatial extent of high-energy regions, the system identifies blooming artifacts as regions whose extent changes with sensitivity, while actual objects maintain consistent spatial extents. This parameter variation enables reliable artifact identification without false positives.
2Reliability
If multiple point clouds are compared with varying sensitivities, then reliability of artifact identification is improved, but device complexity increases
Solution Approach 1:
The patent segments the point cloud data by identifying and extracting high-energy regions (potential artifacts) separately from other regions. By focusing computational resources only on these high-energy regions and comparing their spatial extents across measurements, the system achieves reliable artifact identification without the need to process and compare entire point clouds, thereby managing complexity effectively.
Solution Approach 2:
The patent applies partial action by performing comparisons only on high-energy regions rather than entire point clouds. The system identifies regions with energy above a threshold and limits its artifact detection algorithm to these specific regions, reducing computational complexity while maintaining high reliability in artifact identification for the most problematic areas.
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 enhances the reliability of object detection by accurately filtering out blooming artifacts, improving the quality of point clouds for subsequent processing and enabling safer, more reliable automatic vehicle guidance.
Implementation Method 1
electromagnetic radiation is emitted into an environment of the sensor system for generating a first point cloud during a first measurement period and for generating a second point cloud during a second measurement period, and reflected portions of the emitted radiation are detected
Implementation Method 2
a small portion of the electromagnetic radiation's power is generally absorbed, which can lead to fluctuations in the refractive index and, consequently, to beam distortion. This effect, also known as blooming, is particularly noticeable in highly reflective objects and correspondingly high reflected radiation powers
Data Source
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Figure 2
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AI summary
According to a method for operating a sensor system, radiation is emitted during a first and a second measurement time period in order to generate point clouds, the points of which are described by a spatial position and an energy characteristic. A first sub-quantity (8a) of a first point cloud (8) and a second sub-quantity (9a) of a second point cloud (9) are identified with an energy characteristic which is greater than or equal to an energy threshold in each case. A third sub-quantity (8b, 8c) of the first point cloud (8) and a fourth sub-quantity (9b, 9c) of the second point cloud (9) are identified with positions which lie within the spatial surroundings of the first sub-quantity (8a) and the second sub-quantity (9b), respectively. The spatial extension of the fourth sub-quantity (9b, 9c) is compared with the spatial extension of the third sub-quantity (8b, 8c), and the points of the third and/or the fourth sub-quantity (8b, 8c, 9b, 9c) are marked as artifacts on the basis of the result of the comparison.