5 ms·

Not that forgotten, right? Isn't SLAM almost pure geometry? I don't think stuff like LSD-SLAM or any structure from motion stuff has deep learning built in.

AndrewKemendo 9y ago
Correct about the current state of SLAM. However the next wave for computer vision focused tracking and mapping is heavily based on ML - though there isn't a single combined system that gives robust SLAM/PTAM level results yet.
- logicallee 9y ago
  why would anyone downvote the parent comment (by andrewkemendo) given that they seem to speak with the authority of an expert in the field. thanks for your comment.
osdf 9y ago
There seem to be some nice developments into this direction recently? Something like the SfMNet (https://arxiv.org/abs/1704.07804 https://arxiv.org/abs/1704.07804) or the Correlation Filter Net (http://www.robots.ox.ac.uk/~luca/cfnet.html http://www.robots.ox.ac.uk/~luca/cfnet.html) for tracking.
rasz 9y ago
Most (all?) SLAM approaches are naive and produce point clouds. True geometry approach would result in purely 3d vector output. Kinect, Tango, Hololens all fail at recognizing flat surfaces and straight lines.
- robotresearcher 9y ago
  There exist research SLAM systems that use lines, planes[1] and other geometric objects as features, and thus provide non-point geometric reconstructions. Hell, there's even a demo using entire pieces of office furniture as the localization features [2]. [1] https://www.youtube.com/watch?v=KOG7yTz1iTA https://www.youtube.com/watch?v=KOG7yTz1iTA [2] https://www.youtube.com/watch?v=tmrAh1CqCRo https://www.youtube.com/watch?v=tmrAh1CqCRo
tnecniv 9y ago
With the SLAM algorithms I am familiar, they are mostly statistics and abstracted above the camera component. They are mainly concerned with where am I most likely to be given I am witnessing these previously seen landmarks and have this noise model. Moreover, the chosen statistical model has to be such that it can be updated rapidly as new landmarks and state estimates are added (which typically presents as an increase in dimension until you hit some limit you impose).

AndrewKemendo 9y ago

Correct about the current state of SLAM. However the next wave for computer vision focused tracking and mapping is heavily based on ML - though there isn't a single combined system that gives robust SLAM/PTAM level results yet.

logicallee 9y ago
why would anyone downvote the parent comment (by andrewkemendo) given that they seem to speak with the authority of an expert in the field. thanks for your comment.

osdf 9y ago

There seem to be some nice developments into this direction recently? Something like the SfMNet (https://arxiv.org/abs/1704.07804 https://arxiv.org/abs/1704.07804) or the Correlation Filter Net (http://www.robots.ox.ac.uk/~luca/cfnet.html http://www.robots.ox.ac.uk/~luca/cfnet.html) for tracking.

rasz 9y ago

Most (all?) SLAM approaches are naive and produce point clouds. True geometry approach would result in purely 3d vector output. Kinect, Tango, Hololens all fail at recognizing flat surfaces and straight lines.

robotresearcher 9y ago
There exist research SLAM systems that use lines, planes[1] and other geometric objects as features, and thus provide non-point geometric reconstructions. Hell, there's even a demo using entire pieces of office furniture as the localization features [2]. [1] https://www.youtube.com/watch?v=KOG7yTz1iTA https://www.youtube.com/watch?v=KOG7yTz1iTA [2] https://www.youtube.com/watch?v=tmrAh1CqCRo https://www.youtube.com/watch?v=tmrAh1CqCRo

tnecniv 9y ago

With the SLAM algorithms I am familiar, they are mostly statistics and abstracted above the camera component. They are mainly concerned with where am I most likely to be given I am witnessing these previously seen landmarks and have this noise model. Moreover, the chosen statistical model has to be such that it can be updated rapidly as new landmarks and state estimates are added (which typically presents as an increase in dimension until you hit some limit you impose).