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I think there's still a lot of room for exploiting geometry. In particular, it leads to very simple models that require little training, and most importantly ar
by cmontella 9y ago
I think there's still a lot of room for exploiting geometry. In particular, it leads to very simple models that require little training, and most importantly are completely transparent in how they work. I did some work on a robotic wheelchair that exploited the prevalence of poll-like objects in the environment (trees, parking meters, street lamps) to localize. The model just looked for cylindrical objects and matched them against a map that we generated a priori.
Deep models are best suited when you don't know what features are important in your model. For us, it was straight forward with the radius and orientation of the poll object, so a deep model would have probably been the wrong approach.
- krallja 9y agoI love the idea of using poles as your fiducials.
- trevyn 9y agoThis is a good observation. My personal opinion is that using many smaller networks as components of a larger, manually engineered system could be a fruitful approach for these complex problems. End-to-end deep learning is the holy grail -- and may ultimately happen -- but I don't think computation in silicon semiconductors is going to cut it, and we don't yet know what the next computational substrate will be. Did you use a smallish convolutional model for extracting pole position, orientation, and radius from image data, or another approach?
- cmontella 9y agoIf you're interested, here is a link to the initial paper: http://vader.cse.lehigh.edu/publications/fsr12_mon_per.pdf http://vader.cse.lehigh.edu/publications/fsr12_mon_per.pdf