3D Point Cloud Position Estimation with Dual Sensor Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies for estimating the position and orientation of mobile objects using three-dimensional point cloud data lack accuracy and reliability, particularly in driverless driving scenarios.
Innovation Solution
A system utilizing two distance measurement devices to perform independent matching calculations and combine results based on reliability criteria, with deceleration commands when discrepancies exceed a threshold, to enhance estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple distance measurement devices are used to perform independent matching calculations, then estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the estimation task into multiple independent matching calculations performed by different distance measurement devices. Each device independently calculates position and orientation from point cloud data, and these segmented results are then integrated through the estimation process to achieve higher overall accuracy while maintaining manageable complexity through modular processing
Solution Approach 2:
The system merges the calculation results from multiple independent matching operations into a unified estimate value. By combining the first calculation result from the first distance measurement device and the second calculation result from the second distance measurement device, the system achieves enhanced estimation accuracy that surpasses what any single device could provide alone
2Reliability
If multiple calculation results are combined, then reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary independent matching calculations using multiple distance measurement devices simultaneously. By preparing multiple calculation results in advance through parallel processing, the system can quickly select or combine the most reliable results when needed, reducing the time penalty that would otherwise result from sequential processing
Solution Approach 2:
The estimation process incorporates feedback mechanisms that evaluate the quality and reliability of each calculation result. Based on this feedback, the system intelligently selects or weights results from different devices, avoiding unnecessary processing of low-quality data and thus reducing overall processing time while maintaining high reliability
Data Source
AI summary
An estimation device used to cause a mobile object to move by driverless driving includes one or more processors configured to: perform first matching using three-dimensional point cloud data on the mobile object acquired by a first distance measurement device, and calculate at least one of a position and an orientation of the mobile object as a first calculation result derived from the first matching; perform second matching using three-dimensional point cloud data on the mobile object acquired by a second distance measurement device, and calculate at least one of a position and an orientation of the mobile object as a second calculation result derived from the second matching; and execute an estimation process of calculating an estimate value by using at least one of the first calculation result and the second calculation result, depending on the first calculation result and the second calculation result.