Smartphone Camera Indoor Positioning With AOA and IMU Calibration
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Solution Overview
Problem
Indoor positioning systems face challenges due to high accuracy requirements and lack of dedicated infrastructure, with existing methods requiring complex cameras and image processing, limited field of view, and reliance on multiple beacons within the camera's focus.
Innovation Solution
A system utilizing existing smartphones with a simple angle of arrival method, employing standalone transmitters integrated with existing lighting, and using a processing module to compute position without complex image processing, leveraging a rotational matrix and maximum-likelihood estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a rolling shutter method with rapidly switching LEDs is used to enable indoor positioning, then transmitter identification is improved, but image clarity deteriorates due to fringe patterns
Solution Approach 1:
The patent applies preliminary action by pre-calibrating the inertial measurement unit (IMU) to establish a rotational matrix that maps camera coordinates to world coordinates. This pre-computed transformation matrix is then used during positioning to directly convert image point coordinates into spatial angles without requiring complex real-time image processing, thus resolving the contradiction between achieving accurate transmitter identification and maintaining image clarity.
2Area of stationary object
If the field of view is expanded to capture multiple beacons, then coverage area is improved, but the beacons become blur due to limited focal length
Solution Approach 1:
The patent applies dimensionality change by transitioning from a 2D image plane problem to a 3D spatial problem. Instead of trying to capture all beacons sharply in a 2D image plane (which requires narrow FoV and complex focusing), the system uses the camera's inherent perspective projection to capture beacons at different depths, then uses the pre-calibrated rotational matrix and IMU data to recover 3D spatial angles. This allows wide coverage while maintaining measurement accuracy through mathematical transformation rather than optical focusing.
3Measurement precision
If more beacons are used to improve positioning accuracy, then measurement precision is improved, but device complexity increases due to requiring all beacons within FoV
Solution Approach 1:
The patent applies self-service by utilizing the smartphone's existing sensors (camera, IMU) and pre-calibrated rotational matrix to perform positioning autonomously. The system does not require complex real-time image processing or synchronized beacon communication protocols. Each beacon independently emits light, and the smartphone independently processes the image and IMU data to compute position, eliminating the need for complex inter-device coordination and reducing overall system complexity while maintaining positioning accuracy.
4Ease of operation
If existing smartphones are used without hardware modifications, then ease of operation is improved, but measurement precision may deteriorate due to camera limitations
Solution Approach 1:
The patent applies parameter changes by transforming the problem from one requiring precise optical parameters (focal length, principal point, distortion coefficients) to one that uses the camera's intrinsic projection geometry combined with pre-calibrated IMU rotational matrix. The system accepts the smartphone camera's inherent parameters rather than trying to improve them, and instead changes the approach to use these parameters in a mathematical model that compensates for their limitations. This maintains ease of operation with off-the-shelf devices while achieving sufficient positioning accuracy through algorithmic compensation.
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
Enables accurate indoor positioning without hardware modifications to smartphones, simplifies implementation, and avoids additional light sources, while distinguishing transmitters from mirror images and reducing complexity.
Implementation Method 1
an image capturing module configured to capture an image of at least two transmitters that are provided within the enclosed space
Data Source
Figure 1
Figure 2~3
AI summary
This document describes a system for determining a location of a receiver in an enclosed space whereby its location is obtained based on information associated with at least two transmitters that are provided within the enclosed pace. In particular, once the images of the transmitters are captured by an image capturing module provided within the receiver, the receiver is configured to process the captured image and based on the outcome of this process, determine its location in the enclosed space.