View source for 18047796. SELF-SUPERVISED THREE-DIMENSIONAL LOCATION PREDICTION USING MACHINE LEARNING MODELS simplified abstract (QUALCOMM Incorporated)

=SELF-SUPERVISED THREE-DIMENSIONAL LOCATION PREDICTION USING MACHINE LEARNING MODELS=


==Organization Name==

[[:Category:QUALCOMM Incorporated|QUALCOMM Incorporated]]


[[Category:QUALCOMM Incorporated]]

==Inventor(s)==

[[:Category:Hanno Ackermann of Hilversum (NL)|Hanno Ackermann of Hilversum (NL)]][[Category:Hanno Ackermann of Hilversum (NL)]]

[[:Category:Ilia Karmanov of Amsterdm (NL)|Ilia Karmanov of Amsterdm (NL)]][[Category:Ilia Karmanov of Amsterdm (NL)]]

[[:Category:Farhad Ghazvinian Zanjani of Almere (NL)|Farhad Ghazvinian Zanjani of Almere (NL)]][[Category:Farhad Ghazvinian Zanjani of Almere (NL)]]

[[:Category:Daniel Hendricus Franciscus Dijkman of Haarlem (NL)|Daniel Hendricus Franciscus Dijkman of Haarlem (NL)]][[Category:Daniel Hendricus Franciscus Dijkman of Haarlem (NL)]]

[[:Category:Fatih Murat Porikli of San Diego CA (US)|Fatih Murat Porikli of San Diego CA (US)]][[Category:Fatih Murat Porikli of San Diego CA (US)]]

==SELF-SUPERVISED THREE-DIMENSIONAL LOCATION PREDICTION USING MACHINE LEARNING MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18047796 titled 'SELF-SUPERVISED THREE-DIMENSIONAL LOCATION PREDICTION USING MACHINE LEARNING MODELS

==Simplified Explanation==

The present disclosure describes a method for training a machine learning model to predict the location of a device in a spatial environment.

* The method involves training a generator model to map scene data to points in three-dimensional space.
* Critic models are trained to provide feedback to the generator model, pushing the points in three-dimensional space towards specific planes.
* The generator model is then deployed for predicting the location of a device in a spatial environment.

== Potential Applications ==
This technology has potential applications in various fields, including:

* Robotics: The ability to predict the location of a device in a spatial environment can be valuable for autonomous robots navigating in complex environments.
* Augmented Reality: Predicting device location accurately can enhance the user experience in augmented reality applications by aligning virtual objects with the real world.
* Indoor Navigation: This technology can assist in indoor navigation systems by accurately determining the location of a device within a building or facility.

== Problems Solved ==
The self-supervised training method described in the patent application addresses the following problems:

* Device Localization: By training a machine learning model to predict device location in a spatial environment, this technology solves the problem of accurately determining the position of a device.
* Spatial Understanding: The method helps the machine learning model understand the spatial layout of an environment, including multiple planes, enabling better predictions and decision-making.

== Benefits ==
The use of self-supervised training for device localization offers several benefits:

* Improved Accuracy: By training the model to map scene data to points in three-dimensional space, the accuracy of device location prediction can be significantly improved.
* Versatility: The method can be applied to various spatial environments, including those with multiple discrete planes.
* Real-time Prediction: Once deployed, the trained model can provide real-time predictions of device location, enabling immediate decision-making and action.


==Original Abstract Submitted==

Certain aspects of the present disclosure provide techniques method for self-supervised training of a machine learning model to predict the location of a device in a spatial environment, such as a spatial environment including multiple discrete planes. An example method generally includes receiving an input data set of scene data. A generator model is trained to map scene data in the input data set to points in three-dimensional space. One or more critic models are trained to backpropagate a gradient to the generator model to push the points in the three-dimensional space to one of a plurality of planes in the three-dimensional space. At least the generator is deployed.



[[Category:G06N20/00]]