Robot Glass Surface Detection Using LiDAR and Reflection Verification
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Solution Overview
Problem
Conventional LiDAR and time-of-flight sensors struggle to detect glass and specular surfaces due to their reliance on diffuse reflection, leading to potential collisions and inaccurate localization, as these surfaces often exhibit specular reflection or transmit beams through them, making it difficult for robots to safely navigate environments with glass walls or windows.
Innovation Solution
A method using a time-of-flight sensor that collects measurements, identifies suspicious points based on angular thresholds and spatial separation, and updates a computer-readable map to detect glass and specular surfaces, combined with a camera sensor for verification through reflection detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional LiDAR and time-of-flight sensors are used for robot navigation, then the robot can navigate autonomously, but the sensors fail to detect glass and specular surfaces leading to collisions
Solution Approach 1:
The patent introduces an intermediary verification system that uses multiple sensors (ToF sensor, camera sensor, and polarization sensor) to detect and verify glass surfaces. The camera sensor captures images to verify reflections, and the polarization sensor detects polarization states, serving as intermediary checks to confirm ToF sensor detections of glass surfaces, thereby improving reliability and preventing collisions
Solution Approach 2:
The system implements feedback by continuously monitoring sensor data and updating the environment map in real-time. When glass surfaces are detected through reflection verification and polarization analysis, the system feeds this information back to update the navigation map and alert the robot, creating a closed-loop control system that improves detection accuracy and prevents collisions
2Adaptability or versatility
If ToF sensors rely on diffuse reflection for detection, then the sensors can detect most surfaces, but they cannot detect glass and specular surfaces that exhibit specular reflection or transmit beams
Solution Approach 1:
The patent changes the detection parameters by introducing polarization angle measurement and reflection intensity analysis in addition to time-of-flight measurement. By measuring the polarization state of reflected light and analyzing reflection patterns in camera images, the system can distinguish glass surfaces from other surfaces, thereby improving measurement precision for glass detection while maintaining versatility
Solution Approach 2:
The system adds another dimension to detection by incorporating camera image analysis to verify reflections. This multi-dimensional approach combines ToF distance measurement with visual reflection verification, allowing the system to detect glass surfaces that transmit or specularly reflect beams by looking for characteristic reflection patterns in the image dimension
3Measurement precision
If the robot uses a single sensor type for navigation, then the system is simple, but it cannot accurately distinguish glass surfaces from other surfaces
Solution Approach 1:
The patent implements multi-functionality by using a single integrated system that performs multiple detection functions: the ToF sensor measures distance, the camera sensor verifies reflections, and the polarization sensor detects polarization states. This universal system can detect various surface types (diffuse, specular, transparent) using multiple functions, improving surface material identification accuracy while managing complexity through integrated design
Solution Approach 2:
The system merges multiple sensor types (ToF sensor, camera sensor, polarization sensor) into a unified detection system that shares processing and control resources. By combining these sensors and their data processing functions, the system achieves accurate glass surface identification while reducing overall system complexity through shared architecture and coordinated operation
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
Enables robots to accurately detect and avoid glass surfaces, improving safety and generating precise maps by distinguishing glass from other surfaces, thereby preventing collisions and enhancing navigation in environments with glass objects.
Implementation Method 1
A method using a time-of-flight sensor that collects measurements
Implementation Method 2
these surfaces often exhibit specular reflection or transmit beams through them
Data Source
AI summary
Systems and methods for detecting glass for robots are disclosed herein. According to at least one non-limiting exemplary embodiment, a method for detecting glass objects using a LiDAR or light based time-of-flight (“ToF”) sensor is disclosed. According to at least one non-limiting exemplary embodiment, a method for detecting glass objects using an image sensor is disclosed. Both methods may be used in conjunction to enable a robot to quickly detect, verify, and map glass objects on a computer readable map.