Robot Cleaner 3D Obstacle Detection With Dual Depth Cameras
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Solution Overview
Problem
Conventional robot cleaners face challenges in accurately detecting obstacles and navigating due to reliance on two-dimensional image information, which limits their ability to obtain accurate three-dimensional shape and distance data, and they struggle with increased computation and reduced reaction time when using 3D camera sensors, especially in dark environments.
Innovation Solution
A cleaner equipped with two depth cameras and an infrared projector that alternately captures images and adjusts output to generate three-dimensional coordinate information, allowing for accurate obstacle detection and position recognition without the need for additional sensors, even in dark environments, by using a single set of sensor modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 3D camera sensor is used to acquire three-dimensional coordinate information, then measurement precision of obstacle detection is improved, but device complexity and computation amount increase excessively
Solution Approach 1:
The cleaning robot divides the detection area into multiple regions and uses different detection methods for different regions. For close-range obstacles, ultrasonic sensors detect within a first distance range, while for far-range obstacles, the depth camera detects within a second distance range. This segmentation allows the system to achieve accurate 3D detection without requiring a single complex 3D sensor system to cover all ranges.
Solution Approach 2:
The system transitions from 2D image information to 3D coordinate information by combining depth data from the depth camera with position data. The controller calculates three-dimensional coordinate information based on image information from the depth camera and position information from the cleaning robot, enabling accurate obstacle detection in three-dimensional space without requiring complex 3D sensor arrays.
2Measurement precision
If a 3D camera sensor is used to acquire three-dimensional coordinate information, then measurement precision of obstacle detection is improved, but loss of time increases due to excessive computation
Solution Approach 1:
The system segments obstacle detection into two stages: far-range detection using depth camera for early warning, and close-range detection using ultrasonic sensors for immediate response. This segmentation allows the system to process data more efficiently by using simpler ultrasonic data for immediate obstacle avoidance while using depth camera data for longer-term navigation planning.
Solution Approach 2:
The depth camera continuously monitors the far-range environment to detect obstacles early before they enter the close-range detection zone. By performing preliminary detection at a distance, the system has more time to process information and plan avoidance maneuvers, reducing the critical reaction time when obstacles are detected in the immediate path.
3Device complexity
If a single set of sensor modules is used to acquire three-dimensional information, then device complexity is reduced, but measurement precision deteriorates in dark environments
Solution Approach 1:
The system merges the depth camera with an infrared projector to form a combined sensing module. The infrared projector emits infrared light that illuminates dark areas, and the depth camera captures reflected infrared light to generate depth information. This merging allows the single sensor module to function effectively in both light and dark environments without requiring separate active illumination sensors.
Solution Approach 2:
The infrared projector acts as an intermediary that provides illumination in dark environments. By projecting infrared light onto the environment, it enables the depth camera to capture sufficient reflected light for accurate depth measurement, effectively mediating between the limitation of passive depth sensing and the requirement for operation in dark conditions.
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 solution enables efficient and accurate autonomous traveling and obstacle avoidance by quickly processing three-dimensional coordinate information, reducing manufacturing costs and improving performance while maintaining accurate detection capabilities.
Implementation Method 1
first and second cameras photographing the periphery of the main body
Implementation Method 2
conventional cleaner using the RGB-D camera has a problem in that it cannot sufficiently acquire 3D coordinate information related to an object around a main body in a dark area
Data Source
AI summary
A cleaner performing autonomous traveling includes a main body, first and second cameras photographing the periphery of the main body, and a controller controlling the first and second cameras to capture an image according to preset order, wherein a direction in which the first camera is directed and a direction in which the second camera is directed form a predetermined angle, and the controller generates three-dimensional (3D) coordinate information related to an object located near the main body using images obtained in the first and second cameras.