Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise

Sangli Teng (Berkeley AI Research) · Harry Zhang (Massachusetts Institute of Technology) · David Jin (Massachusetts Institute of Technology) · Ashkan Jasour (Jet Propulsion Laboratory) · Ram Vasudevan (University of Michigan - Ann Arbor) · Maani Ghaffari (University of Michigan) · Luca Carlone (Massachusetts Institute of Technology)

bayes filtersbelief representationconvex optimizationdata associationdistribution approximationlocalizationmax entropy moment kalman filtermoment constraintsmoment-constrained max-entropy distributionnon-gaussian noisenonlinear systemsobservation modelprocess modelrobotics tasksstate marginalizationsymbolic integration

Designing optimal Bayes filters for nonlinear non-Gaussian systems is a challenging task. The main difficulties are: 1) representing complex beliefs, 2) handling non-Gaussian noise, and 3) marginalizing past states. To address these challenges, we focus on polynomial systems and propose the Max Entropy Moment Kalman Filter (MEM-KF). To address 1), we represent arbitrary beliefs by a Moment-Constrained Max-Entropy Distribution (MED). The MED can asymptotically approximate almost any distribution given an increasing number of moment constraints. To address 2), we model the noise in the process and observation model as MED. To address 3), we propagate the moments through the process model and recover the distribution as MED, thus avoiding symbolic integration, which is generally intractable. All the steps in MEM-KF, including the extraction of a point estimate, can be solved via convex optimization. We showcase the MEM-KF in challenging robotics tasks, such as localization with unknown data association.