Self-Calibrating 3D Sensor Intrusion Detection
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Solution Overview
Problem
Traditional 2D camera systems for monitoring volumes provide inadequate depth information, leading to false alerts and cumbersome calibration processes in 3D sensor systems, which require precise calibration and stable sensor positions.
Innovation Solution
A self-calibrated monitoring system using multiple 3D sensors that communicate with a central processing unit, employing a reflective pattern to determine the camera's 3D location within a global coordinate system through alignment of local point clouds with a global tridimensional map, eliminating the need for manual calibration and ensuring accurate depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single 2D camera is used for monitoring, then the system cost is low and setup is easy, but the system provides no depth information leading to false alerts
Solution Approach 1:
The patent combines multiple 3D sensors (laser scanners, depth cameras, or stereo camera pairs) to form a unified monitoring system that captures comprehensive spatial information. By merging data from multiple sensors positioned at different locations, the system achieves both cost-effectiveness and accurate depth measurement, eliminating false alerts while maintaining ease of setup through automated calibration processes.
2Measurement precision
If multiple 3D sensors are used to avoid shadowing effects, then depth information accuracy is improved, but calibration becomes time-consuming and complex
Solution Approach 1:
The patent implements self-calibration functionality where the monitoring system automatically determines its own geometric parameters and sensor positions without requiring manual intervention. The system uses reflective patterns and automated point cloud alignment algorithms to perform calibration tasks that would traditionally require time-consuming manual procedures, thereby maintaining high measurement precision while dramatically reducing calibration time and complexity.
3Reliability
If a uniform coordinate system is defined for all 3D sensors, then measurement merging is reliable, but sensor positions must be fixed and stable which is difficult to guarantee
Solution Approach 1:
The patent transforms the static coordinate system approach into a dynamic solution where the system continuously adapts to sensor position changes. By implementing real-time self-calibration that automatically detects and compensates for sensor movements, the system maintains reliable measurement merging even when sensor positions are not perfectly fixed, thereby preserving reliability while improving ease of operation.
4Measurement precision
If manual calibration with 3D measurement tools is performed, then sensor position accuracy is improved, but the system becomes difficult to manage for layman operators
Solution Approach 1:
The patent empowers the monitoring system to perform its own calibration automatically without requiring external 3D measurement tools or specialized operator knowledge. The system uses built-in reflective patterns, automated point cloud processing, and algorithmic coordinate system alignment to achieve high sensor position accuracy while presenting a simple, user-friendly interface that layman operators can easily manage.
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 reduces calibration errors, simplifies the setup and operation of the monitoring system, and automatically adjusts sensor positions, enhancing reliability and ease of use by providing accurate 3D location and orientation without additional positioning information.
Implementation Method 1
providing a camera comprising at least one reflective pattern such that a data point of said reflective pattern acquired by a tridimensional sensor
Data Source
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AI summary
A method for detecting intrusions in a monitored volume in which: - N tridimensional sensors acquire local point clouds (C) in respective local coordinate systems (S), - a central processing unit (3) receives the acquired local point clouds (C) and, for each sensor (2), computes updated tridimensional position and orientation of the sensor (2) in a global coordinate system (G)of the monitored volume by aligning a local point cloud (C) acquired by said tridimensional sensor with a global tridimensional map (M) of the monitored volume (V), and generates an aligned local point cloud (A) on the basis of the updated tridimensional position and orientation of the sensor (2), - the central processing unit monitors an intrusion in the monitored volume (V) by comparing a free space of the aligned local point cloud (C) with a free space of the global tridimensional map (M).