My robot rotates in rviz slower than in Gazebo
I'm using Ros kinetic, ubuntu 16.04, gazebo 7.16
I want to do navigation. Yet, first of all, I needed to manage tf_tree and rviz. I made tf_tree (odom->base_footprint-->base_link) by using 'ekf_localization' and 'robot_state_publisher' as a transformation respectively. However, When I use ground_truth_to_tf instead of ekf_localization, the problem is solved automatically. Therefore, I think there is a problem about ekf_localization. When I command my robot to move forward or back, there are no problems. But when I order to turn right or left. Gazebo turns more than rviz.
If you need, here is ekf_localization.yaml: For parameter descriptions, please refer to the template parameter files for each node.
ekf_se_odom: frequency: 50 sensor_timeout: 0.1 two_d_mode: false transform_time_offset: 0.0 transform_timeout: 0.0 print_diagnostics: false debug: false
map_frame: map odom_frame: odom base_link_frame: base_link world_frame: odom
odom0: jaguar_velocity_controller/odom odom0_config: [false, false, false, false, false, false, true, true, true, false, false, false, false, false, false] odom0_queue_size: 10 odom0_nodelay: true odom0_differential: false odom0_relative: false
odom2: zed/odom odom2_config: [true, true, false, false, false, false, false, false, false, false, false, false, false, false, false] odom2_queue_size: 10 odom2_nodelay: true odom2_differential: false odom2_relative: false
imu0: imu/data imu0_config: [false, false, false, true, true, true, false, false, false, true, true, true, true, true, true] imu0_queue_size: 10 imu0_nodelay: true imu0_differential: false imu0_relative: false imu0_remove_gravitational_acceleration: true
# use_control: 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 ...
