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The robot_pose_ekf ROS package applies sensor fusion on the robot IMU and odometry values to estimate its 3D pose.

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Udacity - Robotics NanoDegree Program

robot_pose_ekf package

The robot_pose_ekf ROS package applies sensor fusion on the robot IMU and odometry values to estimate its 3D pose.

Nodes

The package contains a single node

  1. robot_pose_ekf: Implements an Extended Kalman Filter, subscribes to robot measurements, and publishes a filtered 3D pose.
    • Script File: wtf.py
    • Subscriber: "/odom", "/imu_data", and "/vo "
    • Publisher: "/robot_pose_ekf/odom_combined"

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Steps to launch the nodes

Step1: Install the package

$ cd /home/workspace/catkin_ws/src/
$ git clone https://github.com/udacity/robot_pose_ekf

Step2: Edit the robot_pose_ekf.launch file

<launch>

<node pkg="robot_pose_ekf" type="robot_pose_ekf" name="robot_pose_ekf">
  <param name="output_frame" value="odom_combined"/>
  <param name="base_footprint_frame" value="base_footprint"/>
  <param name="freq" value="30.0"/>
  <param name="sensor_timeout" value="1.0"/>  
  <param name="odom_used" value="true"/>
  <param name="imu_used" value="true"/>
  <param name="vo_used" value="false"/>

  <remap from="imu_data" to="/mobile_base/sensors/imu_data" />    

</node>

</launch>

Step3: Build the package

$ cd /home/workspace/catkin_ws
$ catkin_make
$ source devel/setup.bash

Step4: Launch the node

$ roslaunch robot_pose_ekf robot_pose_ekf.launch

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The robot_pose_ekf ROS package applies sensor fusion on the robot IMU and odometry values to estimate its 3D pose.

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