Monthly Archives

July 2014

Indoor Navigation Challenges Series – Chapter 1: Finding an accurate location indoors

By | Indoor Positioning Technology

In 2012, a senior Google executive issued an apology to Apple inc. expressing his regret in not warning Apple that mapping and navigation aren’t easy.

The sarcastic apology came in light of the widely regarded failed launch of Apple Maps, the outdoor navigation and mapping application which Apple released with the launch of iPhone 5 and the iOS6 operating system. Evidently, outdoor navigation is not easy.

Furthermore, I would like to say to this Google executive: if you think outdoor navigation is hard, try to map and navigate indoors. It’s an even more elusive challenge. In this series, we describe the challenges and how SPREO has solved them.

Chapter 1:  Finding extremely accurate location

In order to accurately navigate indoors you need positioning accuracy of about 2 meters or better. 2 meters or better allows you to take into account the small frame work of an indoor space. For example it allows you to differentiating between the entrances of two different stores, detect the floor the user is on, and find your car in the parking lot.

Existing outdoor navigation and mapping applications use the Global Positioning Systems (GPS). GPS is a great technology, available in all smartphone devices, and it is successful in navigating and finding locations worldwide. On its best day, GPS has an accuracy of about 5 meters, and sometimes as large as 15 meters, which is clearly over the 2 meters needed for indoor positioning. Accuracy aside, it turns out that GPS cannot even work indoors.

In response, developers have turned to Wi-Fi and Bluetooth sensors to find indoor location. With this comes a separate set of challenges, most obvious of which is that Wi-Fi and Bluetooth technology were built to transfer data, not to show location. Therefore, it comes as no surprise to find that these sensors do not inherently provide accurate location. Manipulating Wi-FI and Bluetooth sensors to work for you in finding location is the first step in achieving accurate indoor location.

This is only the first challenge for those brave enough to take on indoor navigation.

Want to know the next challenge? Wait to read our next post.

– The SPREO development team

Applying Sensor Fusion in Indoor Positioning Prediction Algorithms

By | Digital Wayfinding, Indoor Positioning Technology

Most modern cellular phones and tablets come with numerous sensors. You probably already know about WiFi and Bluetooth and use them everyday, but there are others as well.

For example, current smart devices come with built-in accelerometer, barometer, magnetic sensor and directional sensor. While these devices help fitness apps and the compass do their jobs, the patent-pending SPREO indoor positioning algorithm incorporates these signals for accurate and dynamic indoor position definition. We call it sensor fusion, and we use these additional sensors in addition to Bluetooth Low Energy beacons (“iBeacons”) and Wi-Fi to get accuracy to better than a meter.

The sensor fusion approach does create a unique set of challenges. For example, there is a chance for “accumulated error.” Most people vary their gait (“average step size”) and meander slightly instead of walking a true straight path, especially when talking through crowded hallways and stairs.

We have created a set of discernment rules to interpret the sensors input values and determine whether a step was actually taken in a certain direction or it was just “noise” (irrelevant data inputs). Consider for example how sensor data points would fluctuate if a person was talking with their hands while holding their phone. Another practical example is more subtle and technical: many environments have electrical magnetic resonance which can generate environmental ‘noise’ (data fluctuations).

The correct approach is ultimately a sophisticated algorithm that strategically integrates all available and applicable sensors on smart devices. The key to sensor fusion is to correct and minimize errors in order to achieve accurate position prediction.

We’ve taken indoor location with sensor fusion a step further by developing machine learning algorithms that analyze user behavior and environmental conditions. As a result, our proprietary methodology today provides positioning accuracy under a meter.