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Why odom/filtered output on map frame is moving? [closed]

asked 2022-03-14 13:13:02 -0500

Johart24 gravatar image

Hello, im working on a navigation system.

I am using two instances of ekf filter from robot_localization package. I am obteinig that in the "odom" frame, i receive the gps signal with the odometry_map signal with a good performance, the signals are near enough to the robot whe it is static.

But when i visualice what is happening in the "map" frame, the robot is constantly moving around the ground, and the gps, and the /odometry/global signals are moving together with the robot.

So, the idea that i have is that the "odom -> map" trasnform frame is constantly moving alone. Why i am getting that? Is it becuase the GPS signal ? I am watching that sometimes the robot move in a area that more than 3m of error, which is usually the average gps error.

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Closed for the following reason the question is answered, right answer was accepted by Johart24
close date 2022-05-24 10:42:23.744317

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answered 2022-03-14 13:35:44 -0500

Nikodem gravatar image

This is normal behavior if you configured it according to the documentation.

Idea is that local EKF handles wheel odometry and IMU to provide continuous vehicle trajectory (odom->base transform). Global EKF takes global position from GPS and uses it to provide map->odom transform. This can (and most probably will) jump around due to GPS inaccuracies.

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Hey thanks for answer. I have a concern. If i want to publish a gps coordinate waypoint, this inaccuracies in the map_frame are gonna interfere on the trajectory path creation?

Johart24 gravatar image Johart24  ( 2022-03-14 13:58:10 -0500 )edit

I share you a capture of how it looks like. https://ibb.co/D5Jv1tZ

Johart24 gravatar image Johart24  ( 2022-03-14 15:21:19 -0500 )edit
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answered 2022-03-14 13:55:06 -0500

Johart24 gravatar image

LOCAL EKF

#Configuation for robot odometry EKF LOCAL # frequency: 10 sensor_timeout: 0.1 two_d_mode: true transform_time_offset: 0.0 transform_timeout: 0.0 print_diagnostics: true debug: false

map_frame: map
odom_frame: odom
base_link_frame: base_link
world_frame: odom

# -------------------------------------
# Wheel odometry:

odom0: /odom_enc
odom0_config: [false, false, false,
             false, false, false,
             true,  true,  true,
             false, false, true,
             false, false, false]
odom0_queue_size: 10
odom0_nodelay: true
odom0_differential: false
odom0_relative: false
# imu configure:


imu0: /imu/data_front
imu0_config: [false, false, false,
            false,  false,  false,
            false, false, false,
            false,  false,  true,
            false,  false,  false]
imu0_nodelay: false
imu0_differential: false
imu0_relative: false
imu0_queue_size: 10

#  imu configure:--------------------------------------
imu0: /imu/data
imu0_config: [false, false, false,
            true,  true,  true,
            false, false, false,
            true,  true,  true,
            true,  true,  true]
imu0_nodelay: false
imu0_differential: false
imu0_relative: false
imu0_queue_size: 10
imu0_remove_gravitational_acceleration: false

process_noise_covariance: [1e-3, 0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0,    0,
                         0,    1e-3, 0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0,    0,
                         0,    0,    1e-3, 0,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0,    0,
                         0,    0,    0,    0.3,  0,    0,    0,     0,     0,    0,    0,    0,    0,    0,    0,
                         0,    0,    0,    0,    0.3,  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,    0.5,   0,     0,    0,    0,    0,    0,    0,    0,
                         0,    0,    0,    0,    0,    0,    0,     0.5,   0,    0,    0,    0,    0,    0,    0,
                         0,    0,    0,    0,    0,    0,    0,     0,     0.1,  0,    0,    0,    0,    0,    0,
                         0,    0,    0,    0,    0,    0,    0,     0,     0,    0.3,  0,    0,    0,    0,    0,
                         0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0.3,  0,    0,    0,    0,
                         0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0.3,  0,    0,    0,
                         0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0.3,  0,    0,
                         0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0.3,  0,
                         0,    0,    0,    0,    0,    0,    0,     0,     0,    0,    0,    0,    0,    0,    0.3]
initial_estimate_covariance: [1e-9, 0,    0,    0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,
                            0,    1e-9, 0,    0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,
                            0,    0,    1e-9, 0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,
                            0,    0,    0,    1.0,  0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,
                            0,    0,    0,    0,    1.0,  0,    0,    0,    0,    0,     0,     0,     0,    0,    0,
                            0,    0,    0,    0,    0,    1e-9, 0,    0,    0,    0,     0,     0,     0,    0,    0,
                            0,    0,    0,    0,    0,    0,    1.0,  0,    0,    0,     0,     0,     0,    0,    0,
                            0,    0,    0,    0,    0,    0,    0,    1.0,  0,    0,     0,     0,     0,    0,    0,
                            0,    0,    0,    0,    0,    0,    0,    0,    1.0,  0,     0,     0,     0,    0,    0 ...
(more)
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Asked: 2022-03-14 13:13:02 -0500

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Last updated: Mar 14