Indoor geolocation, defined as the ability to locate and track objects or people inside buildings, has become crucial in various fields such as mobility, logistics, flow management, and the security of goods and people. The use of Artificial Intelligence (AI) in indoor geolocation solutions is not new, and is an integral part of the most powerful and robust solutions on the market. Whether in location engine algorithms or calibration methods, AI makes a significant contribution to the overall improvement of indoor location solutions, and new avenues have opened up with recent developments in the field.
AI makes it possible to optimize localization algorithms by exploiting predictive models based on machine learning. These models can dynamically adapt to changes in the environment, such as the reorganization of interior spaces, the presence of new obstacles, or even fluctuations in wireless radio signals. In this way, location accuracy can be significantly improved.
Indoor environments are often subject to sources of uncertainty, such as propagation anomalies like multipath and signal attenuation. AI techniques, such as neural networks, can be trained to understand and compensate for these sources of uncertainty, thus improving the reliability of location data. Indeed, AI models can be trained to detect and react to dynamic changes in indoor environments, guaranteeing greater robustness in the face of structural modifications or temporary disturbances.
AI can help optimize the energy consumption of geolocation devices, prolonging their autonomy. Intelligent algorithms can be used to determine optimal times for updating positions, thereby reducing energy consumption. However, one of the challenges of recent AI models, such as those based on deep learning, lies in their high consumption of memory or GPU resources, making it difficult to implement them on mobile devices. Solutions based on innovative architectures are therefore needed to optimize energy consumption.
Pole Star, a leading company in the field of indoor geolocation, stands out for its experience in the innovative use of AI. For several years now, the company has been integrating increasingly powerful self-learning models into its solutions, enabling continuous adaptation to changing environments and constant optimization of performance.
By leveraging AI, Pole Star has succeeded in substantially reducing its solution deployment and maintenance costs. Intelligent models can self-adjust, eliminating the need for frequent manual intervention. This has a significant impact on the operational efficiency, scalability and profitability of deployments of the NAO® suite indoor geolocation solution.
In conclusion, AI is a central element of indoor geolocation solutions, offering significant advantages in terms of performance and robustness. Pole Star, as a pioneer in this field, illustrates how AI can be successfully used to meet the challenges of indoor geolocation, in particular to optimize service deployment and maintenance costs. Recent advances in Artificial Intelligence research are paving the way for new perspectives in the constantly evolving field of indoor localization.
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