3D Point Cloud Tracking for Real-Time Obstacle Proximity Alerts
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Solution Overview
Problem
Existing technologies are unable to detect the proximity of a moving object, such as a utility pole during construction, to other obstacles in real time, posing a risk of collision.
Innovation Solution
An obstacle proximity detection device and method that utilizes a three-dimensional laser scanner to acquire point cloud data, specify object and obstacle data, set detection areas, and output alerts based on overlapping or threshold conditions to indicate proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional point cloud data is acquired and analyzed to detect facilities around the road, then the accuracy of facility detection is improved, but real-time detection of moving objects is not achieved
Solution Approach 1:
The detection system segments the point cloud data into multiple types: first point cloud data representing moving objects, second point cloud data representing stationary obstacles, and third point cloud data representing the ground. This segmentation enables specialized processing for each type, allowing real-time tracking of moving objects while maintaining accurate detection of all facilities.
Solution Approach 2:
The system performs preliminary classification of point cloud data into moving objects, obstacles, and ground. By pre-identifying and separating moving object data before detailed analysis, the system can continuously track these objects in real-time while maintaining comprehensive facility detection accuracy.
2Measurement precision
If point cloud data is used to represent three-dimensional space, then detection accuracy is improved, but the complexity of processing and analyzing the data increases
Solution Approach 1:
The system extracts only the necessary features from the comprehensive point cloud data: three-dimensional coordinates and intensity information. By extracting only these essential features rather than processing all raw data points, the system maintains high spatial detection accuracy while reducing processing complexity.
Solution Approach 2:
The system dynamically adjusts the detection process by continuously updating the positions of moving objects based on sequential point cloud data. This dynamic approach allows the system to maintain accurate tracking of moving objects without requiring complex re-processing of entire datasets, as only changes need to be computed.
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 real-time detection of proximity between a moving object and obstacles, ensuring timely alerts to prevent collisions.
Implementation Method 1
an acquisition unit configured to sequentially acquire three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner
Data Source
AI summary
An obstacle proximity detection device includes: an acquisition unit configured to sequentially acquire three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner; a specifying unit configured to specify first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data; a setting unit configured to set a first detection area that is an area around the first point cloud data on the basis of the first point cloud data; a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.