Kalman Filter
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Updated
May 29, 2024 - C++
Kalman Filter
Tracklib library provide a variety of tools, operators and functions to manipulate GPS trajectories
ROS 2 implementation of robotics algorithms based on the Probabilistic Robotics book
This project aims to explore and compare different Kalman filter architectures and their performance on FPGA platforms. The focus is on two main applications: IMU sensor fusion for quadcopters and prediction in power electronics for microgrid renewable energy systems.
Robot platooning, sensor fusion of odometry and inertial unit and more ...
IoT and ML to assuage the uncertainty in city bus schedules. Track live running status and avail tentative schedule of buses. Minimal IoT setup with a central ML-driven web-backend.
Kalman Filter implementations in C++
night sky, kalman, particle
MATLAB code of Extended Kalman Filter (EKF) for Battery State of Charge (SOC) Estimation in Battery Electric Vehicle (BEV)
Project for Master in Electromechanical Engineering at Bruface (ULB-VUB). Includes code for sending IMU data from Arduino Nano 33 BLE to Python via BLE, and then stream it to a LSL Network. STL files are included for 3D printing a box and clamp to attach to a welding gun.
A library for differentiable robotics.
Code for the paper "Computation-Aware Kalman Filtering and Smoothing"
Kálmán filter based ROS 1 / ROS 2 node (geometry_msgs/pose, sensor_msgs/imu)
A generic library for linear and non-linear Gaussian smoothing problems. The code leverages JAX and implements several linearization algorithms, both in a sequential and parallel fashion, as well as efficient gradient rules for computing gradients of required quantities (such as the pseudo-loglikelihood of the system).
Material for the course "Time series analysis with Python"
Distributed Measurement Operator Trainer for Data Assimilation Applications
R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R
Arbitrage-free Dynamic Generalized Nelson-Siegel model of interest rates following Christensen, Diebold and Rudebusch; and its estimation using the Kalman filter / maximum likelihood.
Replication of the research paper : Catching the curl: Wavelet thresholding improves forward curve modelling
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