Object Location Determination Using Multi-Sensor Fusion and Error Function
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Solution Overview
Problem
Current methods for determining the location of container objects in shipping, such as using fixed cameras, are ineffective and inconvenient as they cannot collect overall information about the container, leading to a need for more accurate and efficient location determination.
Innovation Solution
A method that involves collecting measurement locations from a group of sensors for feature marks on the object, generating estimation locations based on offsets, and determining the object location using an error function, allowing for automatic and precise location determination without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed cameras are used to determine container locations, then the system structure is simple, but the location determination accuracy and overall information collection capability are insufficient
Solution Approach 1:
The patent divides the location determination task into multiple segments by using multiple sensors (depth sensor, color sensor, laser scanner) to collect measurements from different perspectives. Each sensor captures specific feature mark information, and the results are integrated through a least squares estimation algorithm to achieve accurate location determination, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent creates a multi-functional sensor system where depth sensors, color sensors, and laser scanners work together to perform multiple functions: capturing geometric information, identifying feature marks, and determining container locations. This integrated approach improves location determination accuracy while maintaining reasonable system complexity through functional consolidation.
2Productivity
If manual methods are used to determine object locations, then the system complexity is low, but the productivity and automation level are reduced
Solution Approach 1:
The patent implements self-service automation where the sensor system automatically collects measurements, processes data through the least squares estimation algorithm, and determines container locations without manual intervention. The system serves itself by integrating sensor data and computing results autonomously, significantly improving productivity while achieving high automation level.
Solution Approach 2:
The patent replaces manual location determination methods with an automated computational system that uses sensor data processing and mathematical algorithms. This substitution of mechanical/manual operations with automated information processing achieves high productivity and automation level, transforming the unloading procedure efficiency.
3Measurement precision
If multiple sensors are used to collect comprehensive measurement data, then the location determination accuracy is improved, but the device complexity and data processing burden increase
Solution Approach 1:
The patent merges data from multiple sensors (depth, color, laser scanner) into a unified measurement system. The least squares estimation algorithm integrates these diverse measurements into a consistent location determination, achieving high accuracy while managing complexity through data fusion rather than separate processing streams.
Solution Approach 2:
The patent transforms raw sensor data into standardized feature mark locations through parameter transformations. By converting measurements from different sensor types into a common coordinate system and using the least squares method to optimize parameters, the system achieves high measurement precision while simplifying the complexity through parameter standardization.
Data Source
AI summary
Embodiments of the present disclosure provide methods for determining an object location of an object. In the method, a group of measurement locations for a group of feature marks in the object are collected from a group of sensors, respectively. A group of estimation locations are obtained for the group of feature marks based on the object location and a group of offsets between the group of estimation locations and the object location, respectively. An error function is generated based on the group of measurement locations and the group of estimation locations. The object location is determined based on the error function. With these embodiments, performance and accuracy for determining the object location may be greatly increased.