LiDAR Work-Area Intrusion Detection with Machine-Data Exclusion
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Solution Overview
Problem
Existing intrusion detection systems fail to accurately distinguish a work machine at work in a work area from other objects entering the area, leading to erroneous detection.
Innovation Solution
An intrusion detection system that utilizes a machine position acquisition unit, point cloud data acquisition, position calculation, and specifying unit to identify the work machine's position and shape, allowing accurate detection of other objects entering the work area by excluding the machine's data from point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If point cloud data is used to detect objects in the work area, then detection coverage is improved, but the work machine is erroneously detected as an intruding object
Solution Approach 1:
The patent extracts and removes the work machine's point cloud data from the overall detection data by comparing against stored machine shape information. This extraction allows the system to detect intruding objects without false alarms from the work machine itself, resolving the contradiction between comprehensive detection coverage and accurate intrusion detection.
Solution Approach 2:
The patent performs preliminary action by pre-storing the work machine's shape data and position information before actual intrusion detection occurs. This preliminary preparation enables the system to automatically identify and exclude the work machine from detection results, ensuring accurate intrusion detection while maintaining full area coverage.
2Measurement precision
If the work machine's position is continuously tracked, then detection precision is improved, but system complexity increases
Solution Approach 1:
The patent makes the position calculation unit multi-functional by having it serve both as a tracking device for monitoring work machine position and as a filtering mechanism for removing machine data from intrusion detection. This universal approach improves position detection precision while avoiding additional complexity from separate dedicated systems.
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
Accurately detects objects other than the work machine entering the work area, preventing false detections by distinguishing the machine's presence within the point cloud data.
Implementation Method 1
a point cloud data acquisition unit that acquires point cloud data indicating a distance from a predetermined reference point provided outside the work area to an object located inside or outside the work area
Data Source
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AI summary
The intrusion detection system includes a GNSS sensor that acquires a position of a work machine (20) at work in a work area (70), a LiDAR (2) that is disposed outside the work area (70) and acquires point cloud data indicating a distance up to an object located inside or outside the work area (70), a position calculation unit that calculates positions of respective points of the point cloud data, a specifying unit that specifies a portion corresponding to the work machine (20) from the point cloud data as specific data, based on the position of the work machine (20) and the positions of the respective points of the point cloud data, and a detection unit that detects that an object has entered the work area (70), based on the point cloud data excluding the specific data.