17954372. INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS simplified abstract (Intel Corporation)
Contents
- 1 INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS - 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 Original Abstract Submitted
INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS
Organization Name
Inventor(s)
Mohammad Haghighipanah of Tigard OR (US)
Rita Chattopadhyay of Chandler AZ (US)
INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17954372 titled 'INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS
Simplified Explanation
The abstract describes a method for motion tracking that involves receiving data from a camera and a LIDAR sensor, transforming the LIDAR data, weighting the data, and combining it to generate image data.
- Camera data and LIDAR data are received.
- LIDAR data is transformed to correspond to the camera data.
- Weighting factors are determined for the camera data and transformed LIDAR data.
- Camera data is weighted and transformed LIDAR data is weighted.
- Weighted data is combined to generate image data.
Potential Applications
This technology could be applied in various fields such as autonomous vehicles, robotics, virtual reality, and augmented reality for accurate motion tracking and scene reconstruction.
Problems Solved
1. Accurate motion tracking in dynamic environments. 2. Integration of data from different sensors for comprehensive scene analysis.
Benefits
1. Improved accuracy in motion tracking. 2. Enhanced scene reconstruction capabilities. 3. Real-time data processing for immediate feedback.
Potential Commercial Applications
Optimizing logistics operations, enhancing security systems, improving gaming experiences, and advancing medical imaging technologies.
Possible Prior Art
Prior art may include methods for sensor fusion, motion tracking algorithms, and image processing techniques used in similar applications.
What are the limitations of this technology in real-world applications?
The limitations of this technology in real-world applications may include: 1. Limited accuracy in complex and rapidly changing environments. 2. High computational requirements for real-time processing.
How does this technology compare to existing motion tracking methods?
This technology offers improved accuracy and reliability by combining data from different sensors, providing a more comprehensive analysis of the environment.
Original Abstract Submitted
A method for motion tracking is provided including receive first data, receive second data, transform the second data to generate transformed second data corresponding to the first frame; determine a first weighting factor for the first data and a second weighting factor for the transformed second data; weight the first data using the first weighting factor to generate first weighted data; weight the transformed second data using the second weighting factor to generate second weighted data; and combine the weighted first data and the weighted second data to generate combined image data. The first data include a first frame of a first scene of an environment detected by a camera or image sensor. The second data include a second frame of a second scene of an environment detected by a light detection and ranging (LIDAR) sensor. At least a subset of the second scene corresponds to the first scene.
