Graph-SLAM

After a trying, but ultimately successful month spent extracting the family from Puerto Rico and re-embedding us in Berkeley, I'm just starting to get back on top of things enough to think about posting...

I had lunch yesterday with a good friend of mine, Pierre, who co-founded a company that specializes in sensory and mapping systems such as those that are used to create Google's "Street View". I was impressed to learn about their system for combining data from GPS, LIDAR, car odometers, and IMUs to create a consistent picture of how a vehicle is located and oriented in space as a function of time. They've spent a lot of time calibrating their systems, and use some sophisticated MCMC post-processing methods for deriving the actual trajectory of a vehicle.

Although the antennas in the PAPER array (that's the low-frequency interferometer I'm working on), are much less mobile than a car, there was considerable overlap between the problem Pierre has been working to solve and the calibration problem I am facing the requires positioning antennas and celesital sources as a function of time in the face of ionospheric distortion, variable gains, etc. Pierre pointed me to Graph-SLAM as a formal description of the problem that we are trying to solve, and suggested that Kalman Filtering with RTS Smoothing was a powerful technique for converging to the optimal solution (with covariance information) in linear time.