filtered odometry overestimation

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So I'm using robot_localization package for localization, with wheel encoders+IMU (Phiget / Realsense IMU) for fusion. The filtered odometry I'm observing is being overestimated by around 15% of that of the wheel odometry. If I drive 10 m straight My filtered odometry tells me I traveled 13.5 m straight.

I tried using both the ekf/ukf localization nodes from the ros package. Here are my parameters:

enter frequency: 20
sensor_timeout: 0.1
two_d_mode: true
transform_time_offset: 0.0
transform_timeout: 0.0
print_diagnostics: true 
debug: false
debug_out_file: /path/to/debug/file.txt
publish_tf: true

map_frame: world              # Defaults to "map" if unspecified
odom_frame: odom            # Defaults to "odom" if unspecified
base_link_frame: base_link  # Defaults to "base_link" if unspecified
world_frame: odom           # Defaults to the value of odom_frame if unspecified

odom0: /burro_base/odom
odom0_config: [false, false, false,
           false, false, false,
           true, true, true,
           true, true, true,
           false, false, false]
odom0_queue_size: 10
odom0_nodelay: true
odom0_differential: false
odom0_relative: false
odom0_pose_rejection_threshold: 10 
odom0_twist_rejection_threshold: 10

imu0: /imu/data_raw
imu0_config: [false, false, false,
          false,  false,  false,
          false, false, false,
          false,  false,  true,
          false,  false,  false]
imu0_nodelay: true
imu0_differential: false
imu0_relative: false
imu0_pose_rejection_threshold: 250
imu0_twist_rejection_threshold: 250
imu0_queue_size: 10
imu0_remove_gravitational_acceleration: true

use_control: false
stamped_control: false
control_timeout: 0.2
control_config: [true, false, false, false, false, true]
acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]  
deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0]


process_noise_covariance: [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,    0.06, 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,    0.1, 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]


initial_estimate_covariance: [1e-9, 0 ...