(Georgia Institute of Technology, 2003)
Dellaert, Frank; Seitz, Steven M.; Thorpe, Charles E.; Thrun, Sebastian
Learning spatial models from sensor data raises the challenging data association
problem of relating model parameters to individual measurements. This paper proposes an
EM-based algorithm, which solves the model learning and the data association problem in
parallel. The algorithm is developed in the context of the the structure from motion problem,
which is the problem of estimating a 3D scene model from a collection of image data. To
accommodate the spatial constraints in this domain, we compute virtual measurements as sufficient
statistics to be used in the M-step. We develop an efficient Markov chain Monte Carlo
sampling method called chain flipping, to calculate these statistics in the E-step. Experimental
results show that we can solve hard data association problems when learning models of 3D
scenes, and that we can do so efficiently. We conjecture that this approach can be applied to a
broad range of model learning problems from sensor data, such as the robot mapping problem.