ekf_localisation without real odom

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Hi everyone, I got a question about the the ekf_localisation; can I use it on a robot without odometry but with an IMU and a laser ? I use hector slam to determine my position.

First I try with just my IMU to test this node, here is my actual launcher:

<launch>

<node pkg="robot_localization" type="ekf_localization_node" name="ekf_localization" clear_params="true">

<param name="frequency" value="10"/>

<param name="sensor_timeout" value="0.1"/>

<param name="two_d_mode" value="false"/>

<param name="odom_frame" value="odom"/>

<param name="base_link_frame" value="imu_link"/>

<param name="world_frame" value="odom"/>

<param name="transform_time_offset" value="0.0"/>

<param name="imu0" value="/imu/data"/>

<rosparam param="imu0_config">[false, false, false,
true,  true,  true,
false, false, false,
true,  true,  true,
true,  true,  true]</rosparam>

<param name="imu0_differential" value="false"/>

<param name="imu0_relative" value="true"/>

<param name="imu0_remove_gravitational_acceleration" value="true"/>

<param name="print_diagnostics" value="true"/>

<!-- ======== ADVANCED PARAMETERS ======== -->

<param name="imu0_queue_size" value="10"/>
<param name="imu0_pose_rejection_threshold" value="0.3"/>
<param name="imu0_twist_rejection_threshold" value="0.1"/>
<param name="imu0_linear_acceleration_rejection_threshold" value="0.1"/>

<param name="debug"           value="false"/>

<param name="debug_out_file"  value="debug_ekf_localization.txt"/>

<rosparam param="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]</rosparam>


<rosparam param="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 ...