GPS integration in robot_localization
Hello all,
I am trying to integrate UBlox Lea6h gps module with robot_localization, this gps is to be mounted on my quadcopter. I am using ros indigo in ubuntu 14.04. By using NavSatFix of rosserial I am feeding lat and long data with a rostopic /fix to the navsat_transform_node. My rostopic echo /fix
output :
header:
seq: 84
stamp:
secs: 1464760028
nsecs: 759104988
frame_id: /fix
status:
status: 0
service: 0
latitude: 19.1345443726
longitude: 72.906288147
altitude: 0.0
position_covariance: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
position_covariance_type: 0
I don't know how to find out and where to write the position covariance. My ekf_localization_node:
<launch>
<node pkg="robot_localization" type="ekf_localization_node" name="ekf_localization" clear_params="true">
<!-- ======== STANDARD PARAMETERS ======== -->
<param name="frequency" value="30"/>
<param name="sensor_timeout" value="0.1"/>
<param name="two_d_mode" value="false"/>
<param name="map_frame" value="map"/>
<param name="odom_frame" value="odom"/>
<param name="base_link_frame" value="base_link"/>
<param name="world_frame" value="map"/>
<param name="transform_time_offset" value="0.0"/>
<param name="odom0" value="/odometry/gps"/>
<rosparam param="odom0_config">[true, true, false, false, false, false, false, false, false, false, false, false, false, false, false]</rosparam>
<param name="odom0_differential" value="false"/>
<param name="odom0_relative" value="false"/>
<param name="print_diagnostics" value="true"/>
<!-- ======== ADVANCED PARAMETERS ======== -->
<param name="odom0_queue_size" value="2"/>
<param name="odom0_pose_rejection_threshold" value="5"/>
<param name="odom0_twist_rejection_threshold" value="1"/>
<param name="debug" value="false"/>
<param name="debug_out_file" value="debug_ekf_localization.txt"/>
<rosparam param="process_noise_covariance">[0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.025, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.025, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.015]</rosparam>
<rosparam param="initial_estimate_covariance">[1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ...