View source for 18470332. METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA simplified abstract (DENSO CORPORATION)

=METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA=


==Organization Name==

[[:Category:DENSO CORPORATION|DENSO CORPORATION]]


[[Category:DENSO CORPORATION]]

==Inventor(s)==

[[:Category:Atsushi Kitayama of Nisshin-shi (JP)|Atsushi Kitayama of Nisshin-shi (JP)]][[Category:Atsushi Kitayama of Nisshin-shi (JP)]]

[[:Category:Hyacinth Mario Arvind Anand of Lindau (DE)|Hyacinth Mario Arvind Anand of Lindau (DE)]][[Category:Hyacinth Mario Arvind Anand of Lindau (DE)]]

[[:Category:Ana-Cristina Staudenmaier of Lindau (DE)|Ana-Cristina Staudenmaier of Lindau (DE)]][[Category:Ana-Cristina Staudenmaier of Lindau (DE)]]

==METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18470332 titled 'METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA

==Simplified Explanation==

A point cloud data is connected with related data, allowing for the prediction of moving routes based on position labels.

* Point cloud data includes three-dimensional position information and acquisition time.
* Multiple sets of point cloud data are prepared.
* Groups are generated by classifying the point cloud data.
* Groups are assigned position labels and moving body labels.
* Moving routes of groups with moving body on-flag are predicted based on position labels.
* Groups are replaced with positions at acquisition time of related data according to the moving route.
* Acquisition time of related data is connected to the group.

== Potential Applications ==
This technology can be applied in various fields such as:
* Autonomous driving systems
* Robotics
* Augmented reality

== Problems Solved ==
This technology helps in:
* Predicting moving routes accurately
* Improving data connectivity and analysis
* Enhancing decision-making processes

== Benefits ==
The benefits of this technology include:
* Increased efficiency in route planning
* Enhanced data visualization
* Improved accuracy in predicting movements

== Potential Commercial Applications ==
This technology can be commercially applied in:
* Transportation and logistics companies
* Urban planning and development firms
* Surveillance and security industries

== Possible Prior Art ==
One possible prior art for this technology could be:
* Research on predictive analytics in transportation systems

== Unanswered Questions ==
=== How does this technology handle real-time data processing? ===
The article does not provide details on the real-time processing capabilities of this technology.

=== What are the potential limitations of this technology in complex environments? ===
The article does not address the potential limitations of this technology in complex and dynamic environments.


==Original Abstract Submitted==

A point cloud data is connected with a related data. Multiple sets of point cloud data are prepared. Each point cloud data includes information of a point cloud connected to three-dimensional position information, and each point cloud data is connected to acquisition time. At least one group is generated by classifying the point cloud, and the group is assigned to a position label and a moving body label. A moving route of the group with a moving body on-flag is predicted based on the position label of the group. The group is replaced with a position at acquisition time of the related data according to the moving route, and the acquisition time of the related data is connected to the group.



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