Muse: Multi-Sensor Integration Strategies Applied To Sequential Monte Carlo Methods

Recursive state estimation is often used to estimate a probability density function of a specific state, e.g. a robot's pose, over time. Compared to Kalman filters, Sequential Monte Carlo (SMC) methods are less constrained in regard to state propagation and update model definition, which makes it easier to implement any suitable problem. In this work, we present a generic Sequential Monte Carlo framework, which uses abstract formulations for importance weighting, propagation and resampling and provides an independent core algorithm that is usable for any problem instantiation, such that diverse SMC problems can be implemented easily and quickly, since the basic algorithms are already provided. Current applications include 2D localization, 2D tracking in a SLAM system and contact point localization on a manipulator surface. Further, we introduce concepts to deal with data input synchronization and fair execution of different weighting models, which makes it possible to incorporate data from as many update sources, e.g. sensors, as desired. As a typical application scenario, we provide a plugin-based and hence easily extensible instantiation for 2D localization and demonstrate the capabilities of our framework and methods based on a well-known dataset.