odometry from fusing odomtry and imu is not good

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Hi, I'm trying use robot_localization to fuse odometry and imu of epson g365, But I meet some issues .I will provide my paramera here.

TF tree

ekf_params

# For parameter descriptions, please refer to the template parameter files for each node.

ekf_se_odom: # Used only for broadcasting odom to base_link transforms
  frequency: 30
  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
  odom0: /wheel_odom
  odom0_config: [false, false, false,
                 false, false, false,
                 true,  true,  true,
                 false, false, true,
                 false, false, false]
  odom0_queue_size: 5
  odom0_nodelay: true
  odom0_differential: true
  odom0_relative: false
  #odom0_pose_rejection_threshold: 5
  #odom0_angular_velocity_rejection_threshold: 0.05
  #odom0_twist_rejection_threshold: 0.3

# --------------------------------------
# imu configure:

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

  # use_control: true
  # control_config: [true,true,false,false,false,true]

  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, 0.05, 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, 0.0255, 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, 0.005]
  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, 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, 1e-9 , 0, 0, 0,    0, 0, 0, 0, 0 ...