Kálmán filter based ROS 1 / ROS 2 node (geometry_msgs/pose, sensor_msgs/imu)
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Updated
May 23, 2024 - C++
Kálmán filter based ROS 1 / ROS 2 node (geometry_msgs/pose, sensor_msgs/imu)
A monocular plane-aided visual-inertial odometry
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.
An open source platform for visual-inertial navigation research.
Self-position estimation by eskf by measuring gnss and imu
Interface for OpenVINS with the maplab project
Apply EKF filter
IMU + X(GNSS, 6DoF Odom) Loosely-Coupled Fusion Localization based on ESKF, IEKF, UKF(UKF/SPKF, JUKF, SVD-UKF) and MAP
System setup for multi robot navigation using tb2. The localization algorithm can choose AMCL or EKF.
UWB EKF positioning. Multi agent case + IMU fusion is extended in the following work: https://github.com/simutisernestas/jubilant-dollop
using hloc for loop closure in OpenVINS
Assignment done as part of COL864 course
3D Pose Estimation of the Planar Robot Using Extended Kalman Filter
Sensei is an open-source Python toolbox for simulating integrated navigation systems and performing analysis to identify, model, and estimate major sources of error in sensor data.
A Master of Engineering Academic Project
Using Kalman Filters for estimating trajectories in linear and non-linear measurement models
A simulator of an autonomous mobile robot which estimates its pose by using Extended Kalman Filter and calculates control input by using Dynamic Window Approach.
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