Progress report 5

Since last time I posted I have been writing more about using non-positioning type sensors in a navigation system. If this approach can be made to work reasonably well, it could be used as a backup in a complete system. I have also been thinking a bit ahead on how to treat data in a system with an arbitrary number of sensors. I have the idea of a model where a position determination is a weighing of different sensor's data, where the weights might perhaps be learnt at runtime, though for example, in an outdoor environment GPS should be the most important, whereas indoors it could be a WLAN positioning system. The model I am thinking of could take other non-positioning sensors into account, which I think could act as a backup, as (if not using vision) we can get a system that is much lighter on computational resources.

This is probably one of the more confusing posts, but I am just starting to think about the model, and more details will come as I go. I'll add a new reference on non-positioning sensors to the bottom of the BiBTeX database right now. The approach used in that paper is machine learning and a "data cooking" module which they claim reduced the navigation error rate of this approach down to 2%.

I will be away for five days. Happy Easter!