MuSe: MultiSensor 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 robotxe2x80x99s 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 pluginbased and hence easily extensible instantiation for 2D localization and demonstrate the capabilities of our framework and methods based on a wellknown dataset.

Learning StateDependent, Sensor Measurement Models for Localization A robot typically relies on sensor measurements to infer its state and the state of its environment. Unfortunately, sensor measurements are noisy, and the amount of noise can vary with state. The literature provides a collection of methods that estimate and adapt measurement noise over time. However, many methods do not assume that measurement noise is stochastic, or they do not estimate sensor measurement bias and noise based on state. In this paper, we propose a novel method called statedependent, sensor measurement models(SDSMMs). This method: 1) learns to estimate measurement probability density functions directly from sensor measurements and 2) stochastically estimates an expected measurement (which includes measurement bias) and a measurement noise, both of which are conditioned upon the states of a robot and its environment. Throughout this paper, we discuss how to learn an SDSMM and use it with the Extended Kalman Filter (EKF). We then apply our method to solve an EKF localization problem using a real robot dataset. Our localization results showed that at least one of our proposed methods outperformed a standard EKF in all 15 cases for 2D position error and 10 of 15 cases for 1D orientation error. Our methods had a mean improvement of 39% for position and 15% for orientation.