Robot Camera Filtering for Sunlight False Positive Suppression

Solution Overview

Problem

Robots equipped with imaging cameras face challenges in navigating due to high intensity noise from sunlight, which causes false positives and inhibits their ability to detect objects accurately, potentially leading to getting stuck.

Innovation Solution

A robotic system and method that dynamically filters high intensity broadband electromagnetic waves by applying a mask to images exceeding a dynamic lighting intensity threshold, adjusting the threshold in real-time based on mean light intensity values from an image frame queue, to eliminate noise and prevent false positives.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If imaging cameras are used to capture images for robot navigation, then the robot can detect objects and navigate, but high intensity sunlight causes noise that generates false positives and reduces detection accuracy

Engineering Contradiction:

Improveobject detection accuracyVSAvoidsunlight noise interference

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary action by capturing a queue of historical images before making detection decisions. By analyzing multiple previous frames to establish baseline lighting conditions and detect trends, the system proactively identifies sunlight interference patterns before they cause false positives, allowing preventive masking of affected regions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback by continuously monitoring image intensity values and comparing them against dynamically updated thresholds derived from historical image data. When sunlight noise is detected through this feedback loop, the system automatically adjusts detection parameters and applies masking to correct for the interference, improving subsequent detection accuracy.

Inventive Principle:

Principle #23Feedback

2Ease of operation

If the robot uses imaging cameras to detect nearby objects, then navigation is enabled, but sunlight noise causes false positives that may cause the robot to get stuck

Engineering Contradiction:

Improvenavigation capabilityVSAvoiddetection reliability

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system performs preliminary action by capturing a queue of historical images before making detection decisions. By analyzing multiple previous frames to establish baseline lighting conditions and detect trends, the system proactively identifies sunlight interference patterns before they cause false positives, allowing preventive masking of affected regions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback by continuously monitoring image intensity values and comparing them against dynamically updated thresholds derived from historical image data. When sunlight noise is detected through this feedback loop, the system automatically adjusts detection parameters and applies masking to correct for the interference, improving subsequent detection reliability.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If a static threshold is used for noise filtering, then the system is simple to implement, but it cannot adapt to changing lighting conditions and fails to eliminate sunlight noise effectively

Engineering Contradiction:

Improvelighting condition adaptationVSAvoidthreshold adjustment mechanism

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system applies dynamics by transitioning from static to dynamic thresholding. The detection threshold is continuously updated based on statistical analysis of historical image intensity values, allowing the system to adapt to changing lighting conditions such as varying sunlight intensity. This dynamic approach maintains detection reliability across different environmental conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes parameters by dynamically adjusting the detection threshold based on environmental conditions. By calculating statistical measures (mean, standard deviation) from historical image data and using these to adapt the threshold, the system responds to parameter changes in lighting conditions while maintaining consistent performance.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11865731B2Systems, apparatuses, and methods for dynamic filtering of high intensity broadband electromagnetic waves from image data from a sensor coupled to a robot

Publication Date: 2024.01.09 BRAIN CORP

AI summary

Systems, apparatuses, and methods for dynamic filtering of high intensity broadband electromagnetic waves in image data from a sensor of a robot are disclosed herein. According to at least one non-limiting exemplary embodiment, sunlight or light emitted from nearby fluorescent lamps may cause a robot to generate false positives of objects nearby the robot as the light may be of high intensity and large bandwidth. These false positives may cause a robot to get stuck or navigate without use of a camera sensor, which may be unsafe.