The last day of ISMAR ‘08 is upon us, and the day opens by stimulating our senses with a session about sensors.

Gabriele Bleser starts this session with a talk about Using the marginalised particle filter for real-time visual-inertial sensor fusion

She starts by showing a short clip with an erratic camera motion that makes everyone dizzie…it actually proves an important capability that she studied which creates less jitter and less requirements imposed on the camera.

She explains the basics of particle filter and the use of inertial measurement.  In the past researchers studied standard particle filter. This is the first study using the a marginalised particle filter.

Testing using the new technique (non linear state space model with linear Gaussian substructure for real time visual inertial pose estimation) with 100 particles resulted in increased robustness against rapid motions.

To prove: Gabriele shows the rapid camera movements once again…

Well, we have to suffer now so that in the future users won’t have to. Kudos Gabriele.

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Next is Daniel Pustka with Dynamic Gyroscope Fusion in Ubiquitous Tracking Environments. This is part of Gudrun Klinker’s journey towards Ubi-AR.

What you need for ubiquitous tracking is automatic discovery of tracking infrastructure, and shield applications from tracking details.

Gyroscopes are very interesting to use (low latency, high update rate, always available), but they have drawbacks (drift, only  for rotation) and are only usable when fused with other sensors.

Daniel and team have proved that the ubiquitous tracking tool set consisting of spatial relationship graphs and patterns is very useful to analyze tracking setups including gyroscopes. It allows a Ubitrack system to automatically infer occasions for gyroscope fusion in dynamically changing tracking situations.

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Jeroen Hol presents Relative Pose Calibration of a Spherical Camera and an IMU

This study builds on the idea that by combining vision and inertial sensors  you get accurate real time position and orientation in a robust and fast motion, and this is very suitable for AR applications. However, calibration is the essential point for this to work.

An easy to use algorithm has been developed and yields results with real data.

Ron Azuma asks: When the image is captured in high motion does it create blur?

Jeroen answers that it can be addressed by changing some parameters.

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Last for this session is Wee Teck Fong from NUS to discuss A Differential GPS Carrier Phase Technique for Precision Outdoor AR Tracking.

The solution that Fong presents provides good accuracy with low jitter, drift and low computational load – and no resolution ambiguities. It works well for outdoor AR apps. With just one GPS you get an accuracy of about 10 meters plus you get high jitter of the tracking. Differential GPS using 2 GPS receivers (low cost 25mm sized) improves the accuracy of tracking. Fong and team have taken it a steps further with an advanced computational model that delivers higher precision for outdoor AR tracking. Fong claims that with a more expensive receiver he can achieve a less than 1mm accuracy, but you can’t use this technique anywhere. An infrastructure of stationary GPS stations transmitting wirelessly could provide a wide constant coverage for this technique.

Fong concludes with a positive note regarding the upcoming European update to the GPS system dubbed Galileo (in 5 years) were things will get significantly better.

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From ISMAR ‘08 Program

• Using the marginalised particle filter for real-time visual-inertial sensor fusion
  Gabriele Bleser, Didier Stricker
• Dynamic Gyroscope Fusion in Ubiquitous Tracking Environments
  Daniel Pustka, Gudrun Klinker
• Relative Pose Calibration of a Spherical Camera and an IMU
  Jeroen Hol, Thomas Schoen, Fredrik Gustafsson
• A Differential GPS Carrier Phase Technique for Precision Outdoor AR Tracking
  Wee Teck Fong, S. K. Ong, A. Y. C. Nee