Real-Time SLAM applied to wearables

Here real-time SLAM is applied to localisation of a wearable camera. The robot has a miniature IEEE1394 camera with a perspective lens. Output from real-time visual SLAM is used to localise the robot and control its fixation point automatically: the robot’s camera can be directed to fixate on any of the feature points in its map as the wearer moves around freely.
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This video, produced by Andrew Davison, Nick Molton and Ian Reid demonstrates the general operation of our single camera localisation and mapping technique and its application to augmented reality and personal localisation. Two new developments beyond the system presented at ICCV2003 here are Nick Molton’s algorithm for feature patch transformation (meaning that features can now be matched over larger camera motions; even where upside-down), and the use of a calibrated wide-angle lens. The augmented reality demonstration shows virtual “kitchen fitting” as furniture is added interactively to live video (by attaching it to automatically-mapped scene features) while the camera continues to move. We also show personal localisation with a passive wearable camera mount built by Walterio Mayol. As before, all processing here runs at 30Hz on a Pentium laptop.

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