HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

State estimation and filtering algorithms in Go

This project implements grid-based FastSLAM1.0 and FastSLAM2.0 algorithms to solve SLAM problem in a simulated environment.

Accelerating Monte Carlo methods for Bayesian inference in dynamical models

SLAM navigation on simplified scenario (FastSLAM implementation using Python) based on Particle Filter (Sequential Monte Carlo). What happens when the visual support of a drone is missing?

A data assimilation experiment with the DALEC ecosystem model

Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables

Simultaneous State Estimation and Dynamics Learning from Indirect Observations.

Hybrid Extended Kalman Filter and Particle Filter. Graded project for the ETH course "Recursive Estimation".

Experiments for online learning and data assimilation for time series data.

Particle Filter estimators using C++ Multibody Dinamics library Simbody

Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals

Kidnapped Vehicle (project 6 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

Localization in a static map, planning in a local map.

Using a 2-dimensional Particle Filter to localize a vehicle

Android applications for SmartPhoneSensing course for indoor localization

Bayesian Particle Learning models in R