Our Visual SLAM technology empowers devices to find the location of any given object with reference to its surroundings and map the environmental layout with only one RGB camera. Our technology allows phones and tablets to instantly track the world around them and overlay digital elements. We use normally GPS applications such as Google Maps to find and get directions to any location and even get personalized data for driving, walking or public Transport. But this GPS technology has its limitations: low precision (~ few meters), the fact that it works only outdoors and poor signal reception in cities with tall buildings due to “urban canyon” effect.
This is not the case with our technology because it helps devices navigate spaces without prior reference points. The potential is resulting in investment – according to a market research report by BIS Research, the Visual SLAM technology market was estimated at $50 million in 2017 and is estimated to reach $8.23 billion by 2027.
As a precursor to our SLAM based wayfinding system, XTEND developed and tested equivalent systems for the Museum of Cultural History (University Of Oslo). This resulted in three successful projects that made it possible for the Museum of Cultural History to test and evaluate the use of new interactive ways for the visitors to experience artefacts in the new Viking Age Museum (2025). We used Trained Model Target Datasets and this means that we used datasets to enable our applications to automatically switch between different objects and/or different Guide Views for each object, depending on what the user points their device at.
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms work by making data-driven predictions or decisions, through building a mathematical model from input data.
The data used to build the final model usually comes from multiple datasets. In particular, three data sets are commonly used in different stages of the creation of the model. The model is initially fit on a training dataset, that is a set of examples used to fit the parameters of the model. Go to http://arim.tech for more info or watch one of our videos to see it working: https://vimeo.com/325846728