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).
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Updated
Jan 1, 2020 - Python
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).
Sensor fusion between IMU, GNSS and Lidar data using an Error State Extended Kalman Filter.
Introducing various sensors to our Gazebo Harmonic simulation
3D Pose Estimation of the Planar Robot Using Extended Kalman Filter
Mobile robots playground
Sensor fusion between Odometry and Lidar data using an Extended Kalman Filter.
UWB EKF positioning. Multi agent case + IMU fusion is extended in the following work: https://github.com/simutisernestas/jubilant-dollop
Implementation for EKF for Visual Inertial Odometry
Robust quadrotor pose estimation in 3D space with a Vision-based observation model and EKF
Using Kalman Filters for estimating trajectories in linear and non-linear measurement models
A simple project demonstrating the robot localization package and EKF (Extended Kalman Filter) node.
System setup for multi robot navigation using tb2. The localization algorithm can choose AMCL or EKF.
IIT(BHU)
Assignment done as part of COL864 course
This project contains code for visual inertial SLAM algorithm using Extended Kalman Filter.
Smartphone-based sensor fusion for real-time robot localization using an Extended Kalman Filter (EKF).
An Extended Kalman Filter Package with a GUI attached to tune covariance values in realtime
This repository accompanies an IROS 2021 submission.
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