An open source platform for visual-inertial navigation research.
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Updated
Jan 21, 2024 - C++
An open source platform for visual-inertial navigation research.
An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! 🛰
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
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).
An extended Kalman Filter implementation in Python for fusing lidar and radar sensor measurements
A monocular plane-aided visual-inertial odometry
Secondary posegraph adapted for interfacing with OpenVINS, based on VINS-Mono / VINS-Fusion.
using hloc for loop closure in OpenVINS
C++ Library for INS-GPS Extended-Kalman-Filter (Error State Version)
Interface for OpenVINS with the maplab project
This code is associated with the paper submitted to Encyclopedia of EEE titled: Robot localization: An Introduction
Sensor fusion between IMU, GNSS and Lidar data using an Error State Extended Kalman Filter.
Self-position estimation by eskf by measuring gnss and imu
3D Pose Estimation of the Planar Robot Using Extended Kalman Filter
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
A Master of Engineering Academic Project
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