[time]
2020-11-09T15:01:19+01:00

[team_name]
WHU

[AutoNavi]


[team_institution]
Wuhan university; AutoNavi

[logolink]


[system_name]


[website]


[track]
3

[reference_person]
Jian Kuang

[email]
kuang@whu.edu.cn

[description]
Brief Introduction: For track 6, a smartphone has many kinds of sensors but their performance is poor, and the smartphone bracket is easy to shake, furthermore, the GNSS signal is interrupted frequently. Therefore, we add the following schemes for the traditional data processing strategy of the vehicle GNSS/INS integrated navigation system. 1) Adjust and estimate the performance parameter of smartphone built-in sensors; 2) Estimate the misalignment angles between smartphone and vehicle; 3) Estimate non-holonomic constraint (NHC) lever arm; 4) Detect the static period of the vehicle and implement ZUPT and ZARU;5) Using magnetic field to restrain heading drift;6) Establish machine learning model to estimate vehicle forward speed. Through the above processing, our algorithm can give continuous and effective positioning results of the scene similar to track 6.

Detailed Introduction: For track 6, a smartphone has many kinds of sensors but their precision is poor, the mobile phone bracket is easy to shake, furthermore, the GNSS signal is interrupted frequently. Therefore, we add the following scheme for the traditional data processing strategy of the vehicle GNSS/INS integrated navigation system.
1)Inertial navigation algorithm. Due to the low performance of the smartphone built-in sensors, the influence of the angular rate and sculling effect caused by the rotation of the earth and motion speed can be ignored. Therefore, the inertial navigation algorithm can be simplified. Meanwhile, the performance parameters of the gyroscope and accelerometer are adjusted according to the three sets of training data given by the competition.
2)State variables. In addition to estimating the vehicle position, velocity, attitude, and sensor bias, considering the instability of the mounting bracket, the misalignment angles are added to the state variables for real-time estimation. Based on the motion characteristics of the vehicle, jumping and lateral drift will not occur in the normal driving process. Assuming that the lateral and vertical velocity of the vehicle rear wheel midpoint is zero, the velocity virtual observation value is constructed to restrict the cumulative drift of speed error (i.e., NHC). Since the mobile phone is installed in the front of the vehicle, the NHC lever arm can’t be ignored, add them to the state variables for real-time estimation, too.
3)ZUPT

[ZARU__The_IMU_original_data_is_used_to_judge_whether_the_vehicle_is_in_a_static_state_or_not__After_the_vehicle_is_detected_to_be_stationary_by_one-second_windows,_the_zero_speed_virtual_observation_value_is_constructed_to_control_the_cumulative_drift_of_speed_error（ZUPT）__At_the_same_time,_the_vehicle_heading_angle_is_constrained_at_the_time_of_stationary（ZARU）_
4)Magnetic_field_update__The_magnetic_field_data_provided_by_the_smartphone_built-in_sensor_is_used_to_calculate_the_change_of_magnetic_field_heading_angle_and_control_the_cumulative_drift_of_vehicle_heading_
5)GNSS_position_update__When_the_GNSS_position_is_used_for_updating_the_estimated_position,_the_lever_arm_between_GNSS_receiver_and_IMU_in_the_mobile_phone_is_ignored,_and_the_GNSS_signal_is_processed_to_eliminate_some_unreliable_GNSS_positions,_avoid_the_negative_effect_of_unreliable_position_on_the_filter_
6)Vehicle_forward_speed_estimation__In_the_case_of_GNSS_signal_interruption,_the_lateral_and_vertical_velocity_of_the_vehicle_body_can_be_restrained_by_NHC,_and_the_heading_angle_drift_of_the_vehicle_can_be_limited_by_magnetic_field_correction,_etc__However,_the_forward_velocity_of_the_vehicle_can’t_be_estimated_correctly__Therefore,_a_supervised_machine_learning_model_is_established__Through_training_and_learning_by_mobile_phone_sensor_data,_a_model_can_be_established_to_estimate_the_forward_speed_of_the_car_according_to_a_sliding_window_of_the_original_sensor_data__This_model_will_be_used_to_constrain_the_forward_speed_of_the_vehicle_to_limit_the_cumulative_drift_
In_conclusion,_through_those_improvements_of_the_GNSS/INS_algorithm,_our_integrated_navigation_solutions_can_give_continuous_and_effective_position,_velocity,_and_attitude_results_of_the_vehicle_based_on_the_smartphone_built-in_sensors_
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