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)
Contents
- 1 METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 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
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD, NON-TRANSITORY COMPUTER READABLE STORAGE MEDIUM, TRAINING DATA SET, AND DEVICE FOR CONNECTING POINT CLOUD DATA WITH RELATED DATA
Organization Name
Inventor(s)
Atsushi Kitayama of Nisshin-shi (JP)
Hyacinth Mario Arvind Anand of Lindau (DE)
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.
