Cliff Detection Using Depth Camera Difference Matrix
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Solution Overview
Problem
Existing cliff detection methods for robots are unreliable due to variations in external conditions such as color depth and lightness, affecting the accuracy of infrared distance sensors.
Innovation Solution
A method using an RGBD depth camera to obtain a detection distance matrix, calculate a theoretical distance matrix, and generate a difference matrix, which is then adjusted with a coefficient matrix to accurately detect cliffs by comparing actual and theoretical distances, thereby reducing the impact of environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If infrared distance sensors are used for cliff detection, then the detection process is simple, but the reliability is poor due to variations in external conditions such as color depth and lightness
Solution Approach 1:
The patent replaces the infrared distance sensor (optical detection method) with a depth camera (image processing method). The depth camera captures images and processes them to detect cliffs, eliminating the sensitivity to light conditions and color variations that plagues infrared sensors. This substitution maintains detection functionality while significantly improving reliability in varying external conditions.
Solution Approach 2:
The patent introduces an intermediary processing step: capturing images with the depth camera, converting them to grayscale, and processing through image algorithms before cliff detection. This intermediary image processing pipeline acts as a mediator that filters out the harmful effects of varying light conditions and color depths, providing stable detection results.
2Reliability
If image processing methods are used for cliff detection, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The depth camera serves multiple functions: it captures images for cliff detection, provides depth information for distance measurement, and can potentially be used for other navigation tasks. By making the depth camera a multi-functional component, the patent justifies the increased device complexity through enhanced versatility and reduced need for separate detection systems.
Solution Approach 2:
The patent transforms the detection approach by changing parameters: instead of measuring infrared reflection intensity (sensitive to light conditions), it processes image grayscale values and depth information (stable across light conditions). This parameter transformation from optical intensity to grayscale/depth metrics improves reliability while managing system complexity through software-based solutions.
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 and accuracy of cliff detection by considering multiple angles and distances, allowing for precise control of robot movement to avoid falls.
Implementation Method 1
obtaining a detection distance matrix of distances between a camera of a target robot and a ground within a preset detection angle range collected by the camera
Data Source
AI summary
The present disclosure provides a method, apparatus, and terminal device for cliff detection. The method includes: obtaining a detection distance matrix of distances between a camera of a target robot and a ground within a preset detection angle range collected by the camera; obtaining a difference matrix obtained by subtracting a theoretical distance matrix from the detection distance matrix; counting an amount of elements in the difference matrix being greater than a preset first threshold; and determining a cliff is detected if the counted amount of the elements is greater than a preset second threshold. Through the overall consideration of the distance matrix of the distances within the detection angle range, even if a certain part of the ground changes its external conditions such as the color depth and the lightness, the influences on the overall detection result is extremely limited, which makes the detection result more accurate and reliable.