Forget GPS Building Indoor Positioning Systems with These Modern Tools

Forget GPS: Building Indoor Positioning Systems with These Modern Tools

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Ever wondered how your phone knows exactly where you are inside huge buildings? Forget GPS – that tech is useless indoors. There are way more advanced technologies powering the latest indoor location services and positioning systems (IPS). You and your customers have probably used a few and did not even realize it.

Whether shopping at the mall, finding your seat at stadium stands, or navigating the airport, machine learning algorithms can track your every move. Using a combination of technologies like Bluetooth, WiFi, and even machine learning, these systems can pinpoint your client’s location within a few meters. Pretty crazy, right? 

This article is built around efficient indoor positioning technologies and their capabilities. We also added a fascinating case study from our experience to provide in-depth insights. Eager to learn more? Let us now begin!

A Custom Indoor Positioning Solution: CHI Software’s Insights and Experience

First things first, why do people even use indoor positioning? The answer is simple – they want more. You see, indoor positioning software comes into play when precision plays a huge role, and the good old GPS just cannot cope. Businesses need more powerful equipment.

Thus, our client, a construction company from Japan, aimed to get complete control over what was going on at the construction site and also gather historical data to assess the results of their work.

Our task was to build custom indoor localization with machine learning (ML), accelerometer, and gyroscope sensors to achieve the highest possible accuracy. 

How to use indoor positioning in your industry? We have some ideas Follow the link

The Development Process: How It Looks from the Inside

Imagine we are on a construction site inside a building. To achieve the project goals, CHI Software engineers use a camera equipped with accelerometers and gyroscopes. We need particularly 360-degree cameras that capture everything around them.

  • Our mission is to create a movement trajectory, or a path, and showcase it on the map of the room where the video was taken. We do this by using the data from the camera’s sensors.

Indoor positioning system (IPS) | CHI Software case study

  • But, creating a trajectory is more complex than it sounds. We need to ensure it is accurate. So, we place special markers on the room walls, which help us detect and recognize the path, correcting the trajectory if needed. This way, we can achieve the desired indoor positioning system accuracy.
  • Now, you might be thinking, “Is it really that simple?” Well, not quite. You see, sensors could be better. They have errors, and they can accumulate them over time. That is when our AI/ML team comes in. They work tirelessly to eliminate errors by filtering data and smoothing out the noise.

Tech stack for an ML-based indoor positioning system | CHI Software insights

  • Currently, our engineers are taking our IPS a step further. We are not just using sensor data anymore, we are also adding video data. CHI engineers are using ArUco markers generated in a special way for this task. 

Another project update we’ve been working on is sensor fusion, a process where data from different sensors is combined to get a more accurate, complete, and dependable picture of the environment or situation. This combination of sensor and video data makes our indoor navigation software more accurate than ever.

Your possibilities are endless if you use modern technologies. So where to start? Ask our experts

By integrating multiple technologies, you can take advantage of the strengths of each to build a robust indoor positioning system tailored to your needs and environment. While there is no one-size-fits-all solution, an accurate IPS is within reach if you have the proper infrastructure and algorithms in place.

What other indoor localization technologies are there? Let us find out about each of them.

The 5 More Indoor Positioning Tools that Can Bring You Success 

The most popular indoor positioning technologies

This section will introduce you to WiFi, Bluetooth, UWB, LiDAR, and AoA, as well as their typical applications and drawbacks to keep in mind.


WiFi-based positioning systems, or WPS, leverage the ubiquitous nature of WiFi networks in our modern world. They function primarily based on the concept of Received Signal Strength Indication (RSSI), a measure of the power level received by the antenna of the WiFi-enabled device, like a smartphone or laptop.

When this device is within range of multiple WiFi access points (APs), it can compare the signal strength from each AP. Think of it as a game of hot and cold. The stronger the signal from a particular AP (the ‘hotter’ it is), the closer you are likely to be.

One of the reasons indoor positioning systems with WiFi are so popular is because they leverage the existing infrastructure. With WiFi APs already installed in many indoor environments, there’s no need for additional hardware.

Any drawbacks?

  • WiFi signals can be affected by interference from other electronic devices or physical obstructions like walls and furniture, which can impact accuracy. 
  • Also, WPS generally provides a range of accuracy from 2 to 15 meters, which might need to be more precise for certain applications.
  • Privacy can be a concern, too, as the system needs to know the location of WiFi APs. 

Bluetooth Low Energy (BLE) Beacons

These beacons are small wireless transmitters that broadcast radio signals over short distances. They operate using the BLE protocol, a power-efficient option of the classic Bluetooth technology. This makes them ideal for applications where power consumption is a concern, as they can last up to several years on a single coin cell battery.

When it comes to indoor localization, BLE beacons work by periodically transmitting a Bluetooth signal received by devices like smartphones or tablets within their range. 

Any drawbacks?

  • Much like WiFi, the accuracy of indoor positioning systems with Bluetooth can be affected by physical obstacles and interference from other electronic devices. 
  • The range of BLE beacons is typically less than that of WiFi, which might require a denser deployment of beacons for full coverage in larger environments. 

LiDAR (Light Detection and Ranging)

This technology sends out pulses of laser light and measures the time it takes for them to bounce back after hitting an object. This info can help you create a detailed 3D map of the surroundings.

LiDAR is like an artist with a very fine brush, painting a highly detailed picture of the environment. Unlike cameras, LiDAR doesn’t need light to “see”, which makes it perfect for dark or dimly lit areas.

It also excels at identifying obstacles, even small ones. So, in a cluttered warehouse, for example, it can help navigate around boxes and equipment with ease.

Any drawbacks?

  • LiDAR tends to be expensive. It’s like buying a high-end smartphone instead of a basic one.
  • Some materials can absorb or reflect laser beams in unpredictable ways, making it challenging to get accurate readings. It is a bit like trying to bounce a ball on a soft sofa; the results can be a bit unpredictable.
  • The detailed data LiDAR provides is fantastic, but it can also be overwhelming. Analyzing it requires powerful software and can be like sifting through a treasure trove of information to find the gems you need.
How to solve the most common ML challenges? Read in our article

Angle of Arrival (AoA)

The fundamental principle behind AoA is that the radio waves from the transmitter will reach different receivers at slightly different times, depending on their distances from the transmitter. AoA is especially useful in environments with lots of obstacles where other technologies might falter.

Any drawbacks?

  • Implementing AoA requires sophisticated hardware and software to accurately measure each receiver’s tiny differences in signal arrival times.  As a result, such systems can potentially be more expensive than other indoor positioning methods.
  • AoA requires a line of sight between the transmitter and the receiver. The system’s accuracy can decrease significantly if the signal’s path is obstructed. 

Ultra-Wideband (UWB)

Finally, let us talk about UWB. It sends billions of pulses across a wide frequency band, measuring the time required for the signal to bounce back. Think of it like a bat using echolocation to find its way in the dark. This technology offers remarkably accurate positioning, often pinpointing your location within a few centimeters. 

This technology’s accuracy is further enhanced by the fact that UWB signals can penetrate walls and other obstacles, making it ideal for indoor positioning systems. It can also measure the direction of arrival of the signals, which allows it to determine not just distance but also the direction of the target.

Any drawbacks?

  • UWB requires dedicated infrastructure. This means installing UWB-enabled devices throughout the area you wish to cover, which can be costly and time-consuming. 
  • The technology is still relatively new and has yet to be more widely adopted, as opposed to other solutions like WiFi or Bluetooth, which could potentially limit its usefulness in certain applications.

And that’s just the tip of the iceberg when it comes to indoor positioning technologies. These technologies continue to evolve as we move forward, providing us with ever more precise, efficient, and user-friendly ways to navigate our world. 

How Does Machine Learning Change the Indoor Positioning Game?

How to use machine learning in indoor localization

Machine learning, a subset of artificial intelligence, is increasingly utilized to power indoor positioning systems. The primary goal is to improve accuracy and reliability, especially in complex environments with numerous obstacles that can interfere with signal propagation.

Not sure how to handle complex AI and ML algorithms? We are here for you! Let's have a short call
  • One common approach is to use machine learning algorithms to predict a device’s location based on the strength of signals received from multiple transmitters, such as WiFi access points or Bluetooth beacons we already mentioned. These algorithms are trained on datasets that map signal strength to specific locations in the indoor environment. They can then use this information to estimate a device’s location even when the signal is influenced by physical obstacles or interference.
  • Another technique involves using machine learning to identify patterns or trends in the movement of people within a building. This can help predict future positions or optimize the building’s layout to facilitate smoother movement.
  • Furthermore, machine learning can also be used to correct errors in positioning data. For instance, it can recognize when a sudden jump in location is likely due to a measurement error rather than actual movement.

Final Words: Weigh Your Needs and Available Resources

Just like in other niches, there is no perfect IPS solution for everyone. Each case is different and involves various factors and scenarios. Do you have a limited budget? Then WiFi can be your go-to choice (but remember about security concerns). 

Maybe you need something more sophisticated and are ready to invest in complex infrastructure? There are AoA or UWB tools to cover your needs.

But if you are ready to move even further, combining several technologies spiced up with machine learning will forever change your operations. The best indoor positioning system looks different for every company.

What we know for sure is that you need help to handle machine learning. Luckily, we can provide just that. CHI Software is the team that makes AI happen. Regardless of your business size and number of features, we are glad to start our work together. All you have to do is schedule your call. Yes, it is that simple.

About the author
Alex Shatalov Data Scientist & ML Engineer

Alex is a Data Scientist & ML Engineer with an NLP specialization. He is passionate about AI-related technologies, fond of science, and participated in many international scientific conferences.

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