View source for Intel corporation (20240111295). INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS simplified abstract

=INTELLIGENT AND ADAPTIVE MULTI-MODAL REAL-TIME SIMULTANEOUS LOCALIZATION AND MAPPING BASED ON LIGHT DETECTION AND RANGING AND CAMERA OR IMAGE SENSORS=


==Organization Name==

[[:Category:intel corporation|intel corporation]]


[[Category:intel corporation]]

==Inventor(s)==

[[:Category:Mohammad Haghighipanah of Tigard OR (US)|Mohammad Haghighipanah of Tigard OR (US)]][[Category:Mohammad Haghighipanah of Tigard OR (US)]]

[[:Category:Rita Chattopadhyay of Chandler AZ (US)|Rita Chattopadhyay of Chandler AZ (US)]][[Category: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 20240111295 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 light detection and ranging (lidar) sensor, transforming the lidar data to correspond to the camera data, weighting the data from both sources, and combining them to generate image data.

* Camera data and lidar data are received.
* Lidar data is transformed to match 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 camera data and weighted lidar data are combined to generate image data.

== Potential Applications ==
This technology can be applied in various fields such as autonomous vehicles, robotics, augmented reality, and virtual reality for accurate motion tracking and scene reconstruction.

== Problems Solved ==
1. Accurate motion tracking in dynamic environments.
2. Seamless 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 fusion for better decision-making.

== Potential Commercial Applications ==
"Motion Tracking Technology for Autonomous Vehicles and Robotics"

== Possible Prior Art ==
Prior art in the field of computer vision and sensor fusion techniques for motion tracking and scene reconstruction may exist, but specific examples are not provided in the abstract.

=== Unanswered Questions ===

=== How does this technology handle occlusions in the scene during motion tracking? ===
The abstract does not mention how the method deals with occlusions when combining data from the camera and lidar sensor.

=== What is the computational complexity of this motion tracking method? ===
The abstract does not provide information on the computational resources required for implementing this technology.


==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.



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