PNI says response time of Sentral depends on the sensors used and comparisons would have to take into account the applicaiton processor being compared. They say more to the point is that Sental gives fast accurate readings with very low power consumption.
Does anyone know what the relative response time would be for a fused sensor set, as opposed to fusing the data on a central CPU? It seems like that type of dedicated attention could raise the responsiveness, particularly for something like a quadcopter. I may have to look into that for one or two projects that I am looking at.
It is also exciting for using only 1% as much power as running the same algorithms on the apps processor. When cool new features are added to mobile devices, there is always the concern that actually using those features puts a big dent in battery life.
Yes, motion tracking for VR is critical, since the user is immersed in the experience. And the sensor fusion algorithms are tricky, since they must use the sensors together to correct potemtial errors in any one.
This is very exciting news for areas like virtual reality. I realize that is only a microscopic fraction of the market, but frankly it is hard to get excited about phones with tighter positional tracking. VR on the other hand is very much in its infancy and things like this are easy to visualize having an impact.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.