Odometry/Filtered of robot_localization is not computing anything

asked 2019-10-17 09:20:18 -0600

enthusiast.australia gravatar image

I am using robot_localization for fusing two input sources. one is a sensor which gives me only x and y positioning information and other is odometry. I have translated both in nav_msgs/oodmetry format with two different topic names. When i use robot_localization with them, it only publishes the exact values, not computing anything. Also if i use positioning information from both of my inputs, it only publishes the exact value of one, completely ignoring the other. Below i have both my msg outputs, Odometry/filtered output and my ekf_template.yaml file. Where i am doing wrong and why is extended kalman filter not computing anything? Ekf file:

frequency: 30
sensor_timeout: 10
two_d_mode: false
transform_time_offset: 0.0
transform_timeout: 0.0
print_diagnostics: true
debug: false
debug_out_file: /home/shairi/catkin_ws/src/debug.txt
publish_tf: true
publish_acceleration: false



#map_frame: map             
odom_frame: odom            
base_link_frame: base_footprint  
world_frame: odom           
base_link_output_frame: base_link
odom0: /odom

odom0_config: [true, true,  false,
               false, false, true,
               false, false, false,
               false, false, false,
               false, false, false]

odom0_queue_size: 5

odom0_nodelay: false

odom0_differential: false

odom0_relative: false
odom1: /odom1
odom1_config: [true, true, false,
               false, false, false,
               false, false, false,
               false, false, false,
               false, false, false]
odom1_differential: false
odom1_relative: false
odom1_queue_size: 2

odom1_nodelay: false


use_control: false
stamped_control: false
control_timeout: 0.2
control_config: [true, false, false, false, false, false]
acceleration_limits: [0, 0.0, 0.0, 0.0, 0.0, 0]
deceleration_limits: [0, 0.0, 0.0, 0.0, 0.0, 0]
acceleration_gains: [0.5, 0.0, 0.0, 0.0, 0.0, 0.5]
deceleration_gains: [0.5, 0.0, 0.0, 0.0, 0.0, 0.5]

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 ...
(more)
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