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
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).
A monocular plane-aided visual-inertial odometry
Self-position estimation by eskf by measuring gnss and imu
Sensor fusion between IMU, GNSS and Lidar data using an Error State Extended Kalman Filter.
C++ Library for INS-GPS Extended-Kalman-Filter (Error State Version)
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
EKF-based LiDAR-Inertial Map matching Localization
Introducing various sensors to our Gazebo Harmonic simulation
#计算机科学#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