Multimodal Robotic Feature Detection for Real-Time 3D Mapping
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Solution Overview
Problem
Conventional methods for detecting features and defects in enclosed spaces like pipelines rely on human intervention, leading to errors and inefficiencies. There is a need for an automated detection method with high accuracy that can be applied across multiple industries and domains.
Innovation Solution
A robotic system equipped with multiple sensors, including visual and infrared cameras, LIDAR, IMU, and motor encoders, which uses deep learning algorithms to detect features and defects. The system tracks the robot's position, recognizes features using sensor data, and creates three-dimensional representations for mapping purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional human intervention methods are used for detecting features and defects, then the system is simpler to implement, but the detection accuracy and efficiency deteriorate due to human errors and manual observation limitations
Solution Approach 1:
The patent replaces manual human observation and mechanical inspection methods with an automated robotic system equipped with multiple sensors (visual cameras, infrared cameras, LIDAR, ultrasonic sensors, gas sensors) and deep learning algorithms. This substitution eliminates human errors and manual limitations while achieving high-precision automated detection of features and defects in pipeline environments.
Solution Approach 2:
The robotic inspection system integrates multiple sensor types and detection modalities into a single platform that can simultaneously perform visual inspection, thermal imaging, distance measurement, and defect detection. This multi-functional approach enables the system to handle various inspection tasks across different pipeline conditions, improving both accuracy and versatility.
2Adaptability or versatility
If multiple sensors are deployed for comprehensive data collection, then the detection capability and feature recognition accuracy improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent combines data from multiple sensor types (visual cameras, infrared cameras, LIDAR, ultrasonic sensors, gas sensors) into a unified processing framework. The deep learning algorithms integrate these disparate data sources to create a comprehensive view of the pipeline environment, enabling the system to detect and analyze features and defects with higher accuracy than any single sensor could achieve alone.
Solution Approach 2:
The patent introduces deep learning algorithms as an intermediary layer that processes and correlates data from multiple sensors. This intermediary processing layer transforms raw sensor data into meaningful feature detections and defect identifications, managing the complexity of multi-sensor integration while maximizing detection capabilities.
3Productivity
If automated deep learning algorithms are used for feature recognition, then the inspection efficiency and accuracy improve, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction at the edge device (robot), preparing processed data for transmission to cloud or server-based deep learning models. This preliminary action reduces the computational burden during real-time operation and enables faster local response while maintaining high accuracy through more intensive processing of pre-prepared data.
Solution Approach 2:
The patent replaces traditional rule-based detection algorithms with deep learning-based automated recognition systems. This substitution enables the system to automatically identify and classify features and defects without manual intervention or complex rule sets, significantly improving inspection efficiency and accuracy despite higher computational requirements.
4Measurement precision
If real-time position tracking is implemented using position sensors, then the mapping accuracy and three-dimensional representation quality improve, but the system complexity and data processing load increase
Solution Approach 1:
The patent uses position sensors (encoders, GPS, or other location-determining devices) as intermediaries to track the robot's movement and determine its position within the pipeline environment. This position information serves as a mediator that correlates sensor data with spatial location, enabling accurate mapping and three-dimensional representation of features and defects without requiring complex tracking mechanisms.
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
The system enables accurate and efficient detection of features and defects in real-time or near real-time, reducing human error and improving inspection efficiency across various industries and domains.
Implementation Method 1
one or more spatial distance sensors such as LIDAR
Implementation Method 2
Other systems utilize infrared cameras to detect temperature variations within the scanned environment
Implementation Method 3
The IMU may include accelerators and gyroscopes
Implementation Method 4
The IMU may include accelerators and gyroscopes
Data Source
AI summary
Robotic systems and associated methods are described herein. The robotic system may collect measurements from various sensors corresponding to motion of the robotic system, the surrounding environment of the robotic system, or both. The robotic system may generate measurement data based on the collected measurements. Measurements from a particular sensor may be processed in conjunction with different sensors of the robotic system, which may facilitate more accurate or more useful measurement data. The systems and methods of the present disclosure enable the detection, labeling, and locating of features in real time or near real time using the robotic system with little or no reliance on human interaction to detect and map the features. The disclosure provides enhanced accuracy and efficiency as it enhances the functionality and reduces the reliance on human detection of features.