Navigating without GPS
In the pre-GPS world, day-to-day navigation was done with a combination of landmarks, maps, signs, and a good sense of direction, a struggle for many who do not have that last item, but with the advent of GPS systems, the necessity for a good sense of direction has become almost moot, other than keeping you from driving into a lake when the navigation system is incorrect. Life without GPS is difficult for those that have grown up with it, and the fact of not having your smartphone with you when traveling almost anywhere is testament to our dependency on GPS navigation, but there are alternatives on the horizon.
In VR systems it is absolutely necessary for the user (and the system) to know where they are spatially, not only for the safety of the VR user but for others that might be negatively affected by wildly flailing arms or other body parts, or for the possible destruction of nearby furniture or pets. Kidding aside, there are a number of systems that are used to gather position information for VR systems, primarily the requirement to mark absolute boundaries before a VR session, or the system’s ability to sense object information around the user using RF, although more sophisticated sensing systems are needed for the advancement of VR into society. That said, the characteristics needed for positioning in AR systems are far more related to everyday location information, as the evolution of AR systems into daily life will necessitate precise positioning information in addition to the visual information seen by the user.
Without location data the AR system will not be able to point you toward a particular destination or help you pick out your rideshare in an airport waiting line, and more importantly, it could point you in the wrong direction if being used in a driving situation. We have noted that Google (GOOG) has been collecting visual data for its ‘Street View’ database since 2007, which it incorporates in Google Maps and Google Earth, and more recently made the 20+m GB of data and 10+m miles of roadway imagery available to developers under the “Live View” API, and while Google is the leader in non-GPS location data, they are certainly not alone. Apple’s geo-anchors use the company’s “Look Around” data, movement indicators, and user imagery to develop a global 3D map, while Facebook (FB) focuses on “Live Maps”, a collection of physical and geometric information, along with a host of other social media oriented companies that are looking for ways to generate location data without using GPS.
The problem stems from the fact that the GPS system relies on signals from at least 4 of the 30+ GPS satellites that orbit our world. There are instances when atmospheric conditions or signal blockage can compromise GPS signals and smartphone GPS data has a 4.9m (16 feet) radius, which could make it a bit difficult to pinpoint a specific parked vehicle or an item in a large warehouse. Niantic (pvt) a small company that was spun out of Google, and financed by Google, Nintendo (7974.JP), and The Pokemon Company (pvt), recently bought 8th Wall (pvt), a company that created an interactive AR development platform that is browser based rather than a standalone application. Niantic’s system is based on geometrics and the system’s understanding of its surroundings along with an understanding of objects themselves, essentially ‘does that object look like something in my database that is defined as a…’, with some of its data collected from Pokemon Go users, a vast network of users who play individually but are now able to play ‘in-network’. As much of the game is based on finding Pokémon hidden in various locations, the visual data that is collected during the games is added to the Niantic database to build out the 3D maps.
While all of the players in the non-GPS location space are approaching it from different angles, the important factor is that the data is robust, giving object recognition and spatially oriented systems more information on which to rely, making their ability to recognize an exact location more precise. As noted Google has a huge amount of data from which its systems can match, and certainly has an advantage over smaller data sets, but tapping into social media or other image sources can build maps quickly with sophisticated algorithms, so there is still no foregone conclusion that one system will rule. The good news is that will all of this data collection, and more socially acceptable hardware for AR, the idea that you could walk down a street wearing AR glasses that tell you how to get to your destination by painting a red dotted line on the sidewalk or indicating which way to go to find that food truck that used to be nearby, is getting to be more of a reality.
In VR systems it is absolutely necessary for the user (and the system) to know where they are spatially, not only for the safety of the VR user but for others that might be negatively affected by wildly flailing arms or other body parts, or for the possible destruction of nearby furniture or pets. Kidding aside, there are a number of systems that are used to gather position information for VR systems, primarily the requirement to mark absolute boundaries before a VR session, or the system’s ability to sense object information around the user using RF, although more sophisticated sensing systems are needed for the advancement of VR into society. That said, the characteristics needed for positioning in AR systems are far more related to everyday location information, as the evolution of AR systems into daily life will necessitate precise positioning information in addition to the visual information seen by the user.
Without location data the AR system will not be able to point you toward a particular destination or help you pick out your rideshare in an airport waiting line, and more importantly, it could point you in the wrong direction if being used in a driving situation. We have noted that Google (GOOG) has been collecting visual data for its ‘Street View’ database since 2007, which it incorporates in Google Maps and Google Earth, and more recently made the 20+m GB of data and 10+m miles of roadway imagery available to developers under the “Live View” API, and while Google is the leader in non-GPS location data, they are certainly not alone. Apple’s geo-anchors use the company’s “Look Around” data, movement indicators, and user imagery to develop a global 3D map, while Facebook (FB) focuses on “Live Maps”, a collection of physical and geometric information, along with a host of other social media oriented companies that are looking for ways to generate location data without using GPS.
The problem stems from the fact that the GPS system relies on signals from at least 4 of the 30+ GPS satellites that orbit our world. There are instances when atmospheric conditions or signal blockage can compromise GPS signals and smartphone GPS data has a 4.9m (16 feet) radius, which could make it a bit difficult to pinpoint a specific parked vehicle or an item in a large warehouse. Niantic (pvt) a small company that was spun out of Google, and financed by Google, Nintendo (7974.JP), and The Pokemon Company (pvt), recently bought 8th Wall (pvt), a company that created an interactive AR development platform that is browser based rather than a standalone application. Niantic’s system is based on geometrics and the system’s understanding of its surroundings along with an understanding of objects themselves, essentially ‘does that object look like something in my database that is defined as a…’, with some of its data collected from Pokemon Go users, a vast network of users who play individually but are now able to play ‘in-network’. As much of the game is based on finding Pokémon hidden in various locations, the visual data that is collected during the games is added to the Niantic database to build out the 3D maps.
While all of the players in the non-GPS location space are approaching it from different angles, the important factor is that the data is robust, giving object recognition and spatially oriented systems more information on which to rely, making their ability to recognize an exact location more precise. As noted Google has a huge amount of data from which its systems can match, and certainly has an advantage over smaller data sets, but tapping into social media or other image sources can build maps quickly with sophisticated algorithms, so there is still no foregone conclusion that one system will rule. The good news is that will all of this data collection, and more socially acceptable hardware for AR, the idea that you could walk down a street wearing AR glasses that tell you how to get to your destination by painting a red dotted line on the sidewalk or indicating which way to go to find that food truck that used to be nearby, is getting to be more of a reality.
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