An open source platform for visual-inertial navigation research.

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

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

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

A monocular plane-aided visual-inertial odometry

using hloc for loop closure in OpenVINS

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

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

Self-position estimation by eskf by measuring gnss and imu

Interface for OpenVINS with the maplab project

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.

This project builds a ROS-based Autonomous Robot from scratch

Kálmán filter based ROS 1 / ROS 2 node (geometry_msgs/pose, sensor_msgs/imu)

This project is an implementation of a Sensor Fusion Module between LIDAR and RADAD sensors for tracking an object; using the Extended Kalman Filter Algorithm.

Extended Kalman Filter Localization Lab using ROS

ekfFusion is a ROS package for sensor fusion using the Extended Kalman Filter (EKF). It integrates IMU, GPS, and odometry data to estimate the pose of robots or vehicles. With ROS integration and support for various sensors, ekfFusion provides reliable localization for robotic applications.

Interactive Simulation for 2D Localization

React.js App for autonomous robot using Extended Kalman FIlter (EKF) and PID controller.

Rowbot is an autonomous rover. It is currently a small scale prototype. My goal is to go bigger!