An open source platform for visual-inertial navigation research.

IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on ESKF, IEKF, UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP

An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! 🛰

Fusing GPS, IMU and Encoder sensors for accurate state estimation.

Accurate 3D Localization for MAV Swarms by UWB and IMU Fusion. ICCA 2018

A monocular plane-aided visual-inertial odometry

State Estimation and Localization of an autonomous vehicle based on IMU (high rate), GNSS (GPS) and Lidar data with sensor fusion techniques using the Extended Kalman Filter (EKF).

using hloc for loop closure in OpenVINS

Interface for OpenVINS with the maplab project

Self-position estimation by eskf by measuring gnss and imu

C++ Library for INS-GPS Extended-Kalman-Filter (Error State Version)

Sensor fusion between IMU, GNSS and Lidar data using an Error State Extended Kalman Filter.

Secondary posegraph adapted for interfacing with OpenVINS, based on VINS-Mono / VINS-Fusion.

An extended Kalman Filter implementation in Python for fusing lidar and radar sensor measurements

3D Pose Estimation of the Planar Robot Using Extended Kalman Filter

This code is associated with the paper submitted to Encyclopedia of EEE titled: Robot localization: An Introduction

A Master of Engineering Academic Project

This project builds a ROS-based Autonomous Robot from scratch

Master Thesis on processing point clouds from Velodyne VLP-16 LiDAR sensors with PCL in ROS to improve localization method, based on Extended Kalman Filter.