A Generalized Extended Kalman Filter Implementation for the Robot Operating System

By Thomas Moore, Daniel W. Stouch. Annual Meeting of the IEEE Industry Applications Society, 2014

482 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques (2018, methodology): “The Robot State Estimator is a standard ROS package [35] which implements an EKF-based estimator with state (cid:4) (cid:5) T are the roll, pitch and yaw Euler angles, p T = p x , p y , p z (cid:5) T represents the position of the robot, and v T = (cid:4) v x , v y , v z , ω x , ω y , ω z (cid:5)…”
Improving RGB-D SLAM in dynamic environments: A motion removal approach (2017, unknown): “by fusing information from the sensors [5].”
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (2020, methodology): “When we receive GPS measurements, we first transform them to the local Cartesian coordinate frame using the method proposed in [21].”
The Marathon 2: A Navigation System (2020, methodology): “general sensor fusion solution using Extended or Unscented Kalman Filters [11].”