Real-Time Pose Estimation

State Estimation and Filtering HW

Sensor fusion in vehicle localisation and tracking is a powerful technique that combines multiple data sources for enhanced accuracy. This project applies and compares two TDOA sensor networks and WLS and Kalman Filter based localisation and tracking techniques.

A visual and educational implementation of Extended Kalman Filter (EKF) SLAM in Python, designed to simulate a mobile robot navigating an unknown 2D environment, detecting landmarks using noisy sensors, and incrementally building a map.

Extended Kalman Filter Project for Self-Driving Car ND using C++

EKF for sensor fusion of Lidar and Radar data

EKF localization system for NAO robot

Excercises for COE-841 @ UFRJ

一个学习项目，尝试复刻Keisuke Fujii《Extended Kalman Filter》论文中的EKF算法，用于二维平面UAV点目标轨迹预测仿真。

masv-platform - Modular Autonomy Simulation & Validation Platform — Sensor Fusion & ML (ROS2 + Local Runner)

Labs for the Robot Learning class, focusing on robotics and Reinforcement Learning. Each lab focuses on a different topic, had mandatory tasks and eventually extensions. All the results have been discussed in the reports.

EKF-based visual-inertial SLAM pipeline that fuses IMU and camera measurements for state estimation, landmark mapping, and trajectory tracking in autonomous robotics applications.

Extended Kalman Filter Implementation for ICM-20948 with STM32-Nucleo using STM32CubeIDE

Altitude and heading reference system

Self-Driving Car Nanodegree Program Extended Kalman Filter Project

Extended Kalman Filter predicting the position of a Bug.

A thesis on the development of a SLAM-based autonomous mobile robot using Arduino and Raspberry Pi

Simple EKF implementation of mobile robot

Code produced as part of the Technical University Berlin Summer Semester 2020 Course "Planetary Exploration and Space Robotics"