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new IMU noise model (#798)
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@angelacmaio
angelacmaio authored Mar 2, 2021
1 parent 8d7573a commit 61d2e7a
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136 changes: 71 additions & 65 deletions submitted_models/cerberus_anymal_b_sensor_config_1/model.sdf
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Expand Up @@ -231,71 +231,77 @@
<always_on>1</always_on>
<update_rate>400</update_rate>
<imu>
<angular_velocity>
<x>
<noise type="gaussian">
<mean>0</mean>
<stddev>2e-4</stddev>
<bias_mean>0.0000075</bias_mean>
<bias_stddev>0.0000008</bias_stddev>
<dynamic_bias_stddev>0.0000004</dynamic_bias_stddev>
<dynamic_bias_correlation_time>1000.0</dynamic_bias_correlation_time>
</noise>
</x>
<y>
<noise type="gaussian">
<mean>0</mean>
<stddev>2e-4</stddev>
<bias_mean>0.0000075</bias_mean>
<bias_stddev>0.0000008</bias_stddev>
<dynamic_bias_stddev>0.0000004</dynamic_bias_stddev>
<dynamic_bias_correlation_time>1000.0</dynamic_bias_correlation_time>
</noise>
</y>
<z>
<noise type="gaussian">
<mean>0</mean>
<stddev>2e-4</stddev>
<bias_mean>0.0000075</bias_mean>
<bias_stddev>0.0000008</bias_stddev>
<dynamic_bias_stddev>0.0000004</dynamic_bias_stddev>
<dynamic_bias_correlation_time>1000.0</dynamic_bias_correlation_time>
</noise>
</z>
</angular_velocity>
<linear_acceleration>
<x>
<noise type="gaussian">
<mean>0</mean>
<stddev>1e-2</stddev>
<bias_mean>0.1</bias_mean>
<bias_stddev>0.001</bias_stddev>
<dynamic_bias_stddev>0.002</dynamic_bias_stddev>
<dynamic_bias_correlation_time>300.0</dynamic_bias_correlation_time>
</noise>
</x>
<y>
<noise type="gaussian">
<mean>0</mean>
<stddev>1e-2</stddev>
<bias_mean>0.1</bias_mean>
<bias_stddev>0.001</bias_stddev>
<dynamic_bias_stddev>0.002</dynamic_bias_stddev>
<dynamic_bias_correlation_time>300.0</dynamic_bias_correlation_time>
</noise>
</y>
<z>
<noise type="gaussian">
<mean>0</mean>
<stddev>1e-2</stddev>
<bias_mean>0.1</bias_mean>
<bias_stddev>0.001</bias_stddev>
<dynamic_bias_stddev>0.002</dynamic_bias_stddev>
<dynamic_bias_correlation_time>300.0</dynamic_bias_correlation_time>
</noise>
</z>
</linear_acceleration>
</imu>
<angular_velocity>
<x>
<noise type="gaussian">
<mean>0</mean>
<stddev>0.009</stddev>
<bias_mean>0.00075</bias_mean>
<bias_stddev>0.005</bias_stddev>
<dynamic_bias_stddev>0.00002</dynamic_bias_stddev>
<dynamic_bias_correlation_time>400.0</dynamic_bias_correlation_time>
<precision>0.00025</precision>
</noise>
</x>
<y>
<noise type="gaussian">
<mean>0</mean>
<stddev>0.009</stddev>
<bias_mean>0.00075</bias_mean>
<bias_stddev>0.005</bias_stddev>
<dynamic_bias_stddev>0.00002</dynamic_bias_stddev>
<dynamic_bias_correlation_time>400.0</dynamic_bias_correlation_time>
<precision>0.00025</precision>
</noise>
</y>
<z>
<noise type="gaussian">
<mean>0</mean>
<stddev>0.009</stddev>
<bias_mean>0.00075</bias_mean>
<bias_stddev>0.005</bias_stddev>
<dynamic_bias_stddev>0.00002</dynamic_bias_stddev>
<dynamic_bias_correlation_time>400.0</dynamic_bias_correlation_time>
<precision>0.00025</precision>
</noise>
</z>
</angular_velocity>
<linear_acceleration>
<x>
<noise type="gaussian">
<mean>0</mean>
<stddev>0.021</stddev>
<bias_mean>0.05</bias_mean>
<bias_stddev>0.0075</bias_stddev>
<dynamic_bias_stddev>0.000375</dynamic_bias_stddev>
<dynamic_bias_correlation_time>175.0</dynamic_bias_correlation_time>
<precision>0.005</precision>
</noise>
</x>
<y>
<noise type="gaussian">
<mean>0</mean>
<stddev>0.021</stddev>
<bias_mean>0.05</bias_mean>
<bias_stddev>0.0075</bias_stddev>
<dynamic_bias_stddev>0.000375</dynamic_bias_stddev>
<dynamic_bias_correlation_time>175.0</dynamic_bias_correlation_time>
<precision>0.005</precision>
</noise>
</y>
<z>
<noise type="gaussian">
<mean>0</mean>
<stddev>0.021</stddev>
<bias_mean>0.05</bias_mean>
<bias_stddev>0.0075</bias_stddev>
<dynamic_bias_stddev>0.000375</dynamic_bias_stddev>
<dynamic_bias_correlation_time>175.0</dynamic_bias_correlation_time>
<precision>0.005</precision>
</noise>
</z>
</linear_acceleration>
</imu>
</sensor>

<sensor name="front_laser" type="gpu_lidar">
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