Actor Detection Using LIDAR and Attribute Data Fusion
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Solution Overview
Problem
Existing systems face challenges in accurately monitoring and identifying actors within physical spaces due to limited information from point cloud data, sparse data collection, and undetected areas caused by the absence or obstruction of sensors.
Innovation Solution
The integration of LIDAR sensors with additional sensors like cameras to combine point cloud data with attribute data for enhanced detection and identification of actors, using attribute data to fill gaps in sensor coverage and improve monitoring across the space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only LIDAR sensors are used for actor detection, then the system structure is simple, but the detection precision and information completeness are insufficient
Solution Approach 1:
The patent combines LIDAR sensors with additional sensors (cameras, microphones, RFID readers) to create a multi-sensor system. This merging allows the system to overcome the limitations of single-sensor detection by integrating complementary data sources, thereby improving actor detection precision and information completeness while managing system complexity through coordinated sensor operation.
2Loss of information
If sensors are sparsely distributed in the environment, then the installation cost is reduced, but undetected areas and data gaps increase
Solution Approach 1:
The patent uses attribute data as an intermediary to bridge gaps in sensor coverage. When LIDAR sensors fail to detect an actor or when additional sensors are obstructed, the system uses attribute information (such as visual features from cameras or RFID tags) to maintain continuous tracking and identification, thereby reducing information loss without requiring dense sensor distribution.
3Loss of information
If additional sensors are integrated to improve detection, then the information completeness increases, but the system complexity and processing requirements increase
Solution Approach 1:
The patent implements a unified processing system that handles multiple sensor types (LIDAR, cameras, microphones, RFID) through common data structures and processing algorithms. This multi-functional approach allows the system to process diverse sensor inputs efficiently, reducing the complexity burden of integrating additional sensors while maintaining comprehensive attribute data collection.
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 enables more accurate and comprehensive monitoring of actors' locations and attributes, overcoming limitations of sparse data and sensor obstructions, thereby improving detection and identification efficiency.
Implementation Method 1
depth sensors, such as LIDAR sensors, that receive point cloud data representative of an actor at a location
Data Source
AI summary
Example systems and methods are disclosed for associating detected attributes with an actor. An example method may include receiving point cloud data for a first actor at a first location within the environment. The method may include associating sensor data from an additional sensor with the first actor based on the sensor data being representative of the first location. The method may include identifying one or more attributes of the first actor based on the sensor data. The method may include subsequently receiving a second point cloud representative of a second actor at a second location within the environment. The method may include determining, based on additional sensor data from the additional sensor, that the second actor has the one or more attributes. The method may include providing a signal indicating that the first actor is the second actor based on the second actor having the one or more attributes.