Nvidia corporation (20240104879). MULTI-MODAL SENSOR CALIBRATION FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 MULTI-MODAL SENSOR CALIBRATION FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-MODAL SENSOR CALIBRATION FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS - 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 How does this technology compare to existing calibration methods for interior sensors in monitoring systems?
- 1.11 What are the specific industries or applications where this technology is most beneficial?
- 1.12 Original Abstract Submitted
MULTI-MODAL SENSOR CALIBRATION FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Hairong Jiang of Campbell CA (US)
Yuzhuo Ren of Sunnyvale CA (US)
Nitin Bharadwaj of Cupertino CA (US)
Chun-Wei Chen of San Jose CA (US)
Varsha Chandrashekhar Hedau of Sunnyvale CA (US)
MULTI-MODAL SENSOR CALIBRATION FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104879 titled 'MULTI-MODAL SENSOR CALIBRATION FOR IN-CABIN MONITORING SYSTEMS AND APPLICATIONS
Simplified Explanation
The abstract of the patent application describes calibration techniques for interior depth sensors and image sensors used in in-cabin monitoring systems. By using calibration targets distributed within the interior space, an intermediary coordinate system is generated to reference 3D positions of features detected by both types of sensors. Rotation-translation transforms are then determined to compute transforms between the sensors' coordinate systems and the intermediary coordinate system.
- Calibration techniques for interior depth sensors and image sensors in in-cabin monitoring systems
- Generation of an intermediary coordinate system using calibration targets within the interior space
- Computation of transforms between the sensors' coordinate systems and the intermediary coordinate system
Potential Applications
The technology described in the patent application could be applied in various industries such as automotive, security, and healthcare for in-cabin monitoring systems.
Problems Solved
The calibration techniques outlined in the patent application help ensure accurate and reliable data from interior depth sensors and image sensors, improving the overall performance of in-cabin monitoring systems.
Benefits
- Enhanced accuracy and reliability of data from interior sensors - Improved performance of in-cabin monitoring systems - Increased safety and security in various applications
Potential Commercial Applications
"Calibration Techniques for Interior Sensors in In-Cabin Monitoring Systems" could be used in industries such as automotive (for driver monitoring systems), security (for surveillance systems), and healthcare (for patient monitoring).
Possible Prior Art
One possible prior art could be the use of calibration targets for sensor calibration in other monitoring systems or applications.
Unanswered Questions
How does this technology compare to existing calibration methods for interior sensors in monitoring systems?
The article does not provide a direct comparison to existing calibration methods, leaving the reader to wonder about the specific advantages or differences of this technology.
What are the specific industries or applications where this technology is most beneficial?
While the article mentions potential applications in automotive, security, and healthcare, it does not delve into specific use cases or industries where this technology could have the most significant impact.
Original Abstract Submitted
in various examples, calibration techniques for interior depth sensors and image sensors for in-cabin monitoring systems and applications are provided. an intermediary coordinate system may be generated using calibration targets distributed within an interior space to reference 3d positions of features detected by both depth-perception and optical image sensors. rotation-translation transforms may be determined to compute a first transform (h1) between the depth-perception sensor's 3d coordinate system and the 3d intermediary coordinate system, and a second transform (h2) between the optical image sensor's 2d coordinate system and the intermediary coordinate system. a third transform (h3) between the depth-perception sensor's 3d coordinate system and the optical image sensor's 2d coordinate system can be computed as a function of h1 and h2. the calibration targets may comprise a structural substrate that includes one or more fiducial point markers and one or more motion targets.
