Ambient Light Sensor Localization for Mobile Devices
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Solution Overview
Problem
Existing localization techniques for mobile devices, such as smartphones, are inefficient and power-intensive, particularly indoors where GPS signals are weak, and fail to provide accurate and fast localization in real-time environments.
Innovation Solution
The use of ambient light data from ambient light sensors and motion data from inertial measurement units (IMUs) for quick and approximate localization, leveraging fixed light sources for triangulation, which reduces power consumption and computation requirements compared to camera-based methods like visual inertial odometry (VIO).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera-based visual inertial odometry (VIO) is used for localization, then measurement precision is improved, but use of energy and computation requirements increase significantly
Solution Approach 1:
The patent extracts only the essential localization function from the complex VIO system by using ambient light sensors to detect fixed light sources. Instead of processing full images and multiple sensor inputs as VIO does, the system extracts luminance data from ambient light sensors and uses it directly to estimate device position relative to known light source locations, dramatically reducing computational load and power consumption while maintaining acceptable localization accuracy
Solution Approach 2:
The patent replaces expensive, power-intensive camera-based VIO processing with inexpensive ambient light sensor readings. The ambient light sensor consumes minimal power compared to the camera and complex processing required for VIO, providing a cost-effective and energy-efficient alternative for localization that doesn't require processing dense pixel data
2Measurement precision
If camera-based visual inertial odometry (VIO) is used for localization, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential localization function from the complex VIO system by using ambient light sensors to detect fixed light sources. Instead of processing full images and multiple sensor inputs as VIO does, the system extracts luminance data from ambient light sensors and uses it directly to estimate device position relative to known light source locations, dramatically reducing computational load and power consumption while maintaining acceptable localization accuracy
Solution Approach 2:
The patent substitutes the mechanical/optical processing system of camera-based VIO with an electrical sensing approach using ambient light sensors. This replacement eliminates the need for complex image processing pipelines, feature extraction algorithms, and inertial sensor fusion required by VIO, simplifying the overall system architecture while achieving the same localization objective
3Ease of operation
If GPS is used for localization, then ease of operation is improved, but measurement precision deteriorates indoors due to signal degradation
Solution Approach 1:
The patent introduces fixed light sources as intermediary reference points for indoor localization. Instead of relying on external GPS satellites that cannot penetrate building structures, the system uses locally-available light sources (lamps, ceiling lights, windows) as mediators to establish position references within the indoor environment, enabling accurate localization without requiring direct line-of-sight to external sources
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate localization of mobile devices in 3D coordinate systems, supporting real-time extended reality (XR) applications with lower power consumption and faster computation, while maintaining continuity during intermittent VIO tracking.
Implementation Method 1
acquiring ambient light data from the ambient light sensor during movement of the device in the physical environment. The ambient light data corresponds to diffuse light received by the ambient light sensor in the physical environment
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods that estimate a location of a light source based on ambient light data. For example, an example process may include acquiring ambient light data from an ambient light sensor (ALS) during movement of a device in a physical environment, acquiring motion data from a motion sensor during the movement of the device, determining, based on the ambient light data and the motion data, estimates of three-dimensional (3D) locations of a light source with respect to the device during the movement of the device, and tracking a location of the device in a 3D coordinate system during the movement of the device based on the estimates of the 3D locations of the light source with respect to the device during the movement of the device.