How AI is Revolutionizing Indoor Positioning
Tech
Beyond Limits: The Power of AI in Indoor Positioning
In today’s industrial landscape, real-time indoor location data has become essential for managing assets and human resources effectively. However, traditional GPS technology faces a fundamental barrier: it cannot function indoors where satellite signals are blocked.
To address this, wireless signals like Wi-Fi and BLE, along with various sensors, have been introduced. Yet, maintaining accuracy while reducing maintenance costs in complex indoor environments remains a significant challenge.
IPIN LABS solves these physical and economic hurdles through Artificial Intelligence. Rather than simply receiving signals, our AI intelligently analyzes and learns from data to perceive spaces. This allows us to provide companies with high-precision indoor positioning solutions at a significantly lower cost.
Automating Data Labeling via Self-Supervised Learning
The most time-consuming and expensive part of setting up an indoor positioning system is "labeling," the process of measuring signals at every point in a facility and matching them to coordinates. IPIN LABS eliminates this inefficiency by applying Self-Supervised Learning AI algorithms.
This approach allows the AI to analyze correlations between raw wireless signal data and learn patterns on its own, without requiring manual data entry.
As a result, the setup period for large-scale industrial sites, which typically takes weeks, can be reduced to less than 24 hours. This drastically lowers data collection costs and minimizes the operational burden on our clients.
RF-SLAM: Low-Cost, High-Efficiency Spatial Awareness
While traditional SLAM (Simultaneous Localization and Mapping) technology relies on expensive equipment like LiDAR or cameras, IPIN LABS’ RF-SLAM solves the cost issue through software and data fusion.
Our RF-SLAM utilizes existing Wi-Fi/BLE signals (RF) already installed in buildings combined with movement data (IMU) from mobile devices.
By integrating these diverse data points, we minimize errors and achieve accurate spatial recognition without the need for additional infrastructure or expensive sensors. This creates a highly efficient solution that is accessible to more businesses.
AI-IMU and Correcting Accumulated Errors
When tracking movement indoors, IMUs (Inertial Measurement Units) estimate position by measuring acceleration and rotation. However, these sensors suffer from "drift," where small errors accumulate over time. IPIN LABS compensates for these physical hardware limitations using a deep learning algorithm known as AI-IMU.
Instead of relying solely on complex physics calculations, our deep learning-based navigation technology allows the AI to learn from vast amounts of movement data to filter out sensor noise and correct errors.
By applying these cutting-edge AI techniques, IPIN LABS provides the most optimized path-tracking performance available.
Completing the Digital Transformation of Indoor Spaces
Using AI in indoor positioning goes beyond simple technical patches. It creates a foundation for digitizing and intelligently managing physical spaces. Through advanced technologies like self-supervised learning, RF-SLAM, and AI-IMU correction, IPIN LABS has successfully balanced the two critical factors of cost and accuracy.
Our AI solutions provide seamless location information in even the most complex environments. We will continue to be a key driver of Digital Transformation (DX) across various sectors, including hospitals, factories, and airports.
IPIN LABS is here to help you prove the hidden value of your space through data.
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