On 21/03/07, Ben Goertzel <[EMAIL PROTECTED]> wrote:
* use a combination of lidar and camera input * write code that took this combined input to make a 3D contour map of the perceived surfaces in the world * use standard math transforms to triangulate this contour map * use some AI heuristics (with feedback from the more general AI routines) to approximate sets of these little triangles by larger polygons * finally, feed these larger polygons into the "polygon vision" module we have designed for NM in a sim-world context
This is very much the traditional machine vision approach, described by Moravec and others and used with some success recently in the DARPA Grand Challenge. I'm also following the same approach which is a very straightforward application of standard engineering techniques. The logistics of doing this are quite complicated, involving camera calibration, correspondence matching and probabilistic spatial modelling and I think the sheer complexity (and drudgery) of the programming task is the reason why few people have ever attempted to do this so far. Being able to create large scale voxel models which can be maintained in a computationally efficient manner suitable for real time use also involves some fancy algorithms. I would agree that where things start to become interesting are at the polygon level, but you still need to maintain an underlying voxel model of space because you can't calculate probability distributions accurately using polygons alone. ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
