Overview

The Extend Kalman Filter Localizer estimates robust and less noisy robot pose and twist by integrating the 2D vehicle dynamics model with input ego-pose and ego-twist messages. The algorithm is designed especially for fast-moving robots such as autonomous driving systems.

Flowchart

The overall flowchart of the autoware_ekf_localizer is described below.

Features

This package includes the following features:

• Time delay compensation for input messages, which enables proper integration of input information with varying time delays. This is important especially for high-speed moving robots, such as autonomous driving vehicles. (see the following figure).
• Automatic estimation of yaw bias prevents modeling errors caused by sensor mounting angle errors, which can improve estimation accuracy.
• Mahalanobis distance gate enables probabilistic outlier detection to determine which inputs should be used or ignored.
• Smooth update, the Kalman Filter measurement update is typically performed when a measurement is obtained, but it can cause large changes in the estimated value, especially for low-frequency measurements. Since the algorithm can consider the measurement time, the measurement data can be divided into multiple pieces and integrated smoothly while maintaining consistency (see the following figure).
• Calculation of vertical correction amount from pitch mitigates localization instability on slopes. For example, when going uphill, it behaves as if it is buried in the ground (see the left side of the "Calculate delta from pitch" figure) because EKF only considers 3DoF(x,y,yaw). Therefore, EKF corrects the z-coordinate according to the formula (see the right side of the "Calculate delta from pitch" figure).

Node

Subscribed Topics

Name	Type	Description
measured_pose_with_covariance	geometry_msgs::msg::PoseWithCovarianceStamped	Input pose source with the measurement covariance matrix.
measured_twist_with_covariance	geometry_msgs::msg::TwistWithCovarianceStamped	Input twist source with the measurement covariance matrix.
initialpose	geometry_msgs::msg::PoseWithCovarianceStamped	Initial pose for EKF. The estimated pose is initialized with zeros at the start. It is initialized with this message whenever published.

Published Topics

Name	Type	Description
ekf_odom	nav_msgs::msg::Odometry	Estimated odometry.
ekf_pose	geometry_msgs::msg::PoseStamped	Estimated pose.
ekf_pose_with_covariance	geometry_msgs::msg::PoseWithCovarianceStamped	Estimated pose with covariance.
ekf_biased_pose	geometry_msgs::msg::PoseStamped	Estimated pose including the yaw bias
ekf_biased_pose_with_covariance	geometry_msgs::msg::PoseWithCovarianceStamped	Estimated pose with covariance including the yaw bias
ekf_twist	geometry_msgs::msg::TwistStamped	Estimated twist.
ekf_twist_with_covariance	geometry_msgs::msg::TwistWithCovarianceStamped	The estimated twist with covariance.
diagnostics	diagnostics_msgs::msg::DiagnosticArray	The diagnostic information.

Published TF

• base_link TF from map coordinate to estimated pose.

Functions

Predict

The current robot state is predicted from previously estimated data using a given prediction model. This calculation is called at a constant interval (predict_frequency [Hz]). The prediction equation is described at the end of this page.

Measurement Update

Before the update, the Mahalanobis distance is calculated between the measured input and the predicted state, the measurement update is not performed for inputs where the Mahalanobis distance exceeds the given threshold.

The predicted state is updated with the latest measured inputs, measured_pose, and measured_twist. The updates are performed with the same frequency as prediction, usually at a high frequency, in order to enable smooth state estimation.

Parameter description

The parameters are set in launch/ekf_localizer.launch .

For Node

Name	Type	Description	Default	Range
show_debug_info	boolean	Flag to display debug info	0	N/A
predict_frequency	float	Frequency for filtering and publishing [Hz]	50	N/A
tf_rate	float	Frequency for tf broadcasting [Hz]	50	N/A
extend_state_step	integer	Max delay step which can be dealt with in EKF. Large number increases computational cost.	50	N/A
enable_yaw_bias_estimation	boolean	Flag to enable yaw bias estimation	1	N/A

For pose measurement

Name	Type	Description	Default	Range
pose_additional_delay	float	Additional delay time for pose measurement [s]	0	N/A
pose_measure_uncertainty_time	float	Measured time uncertainty used for covariance calculation [s]	0.01	N/A
pose_smoothing_steps	integer	A value for smoothing steps	5	N/A
pose_gate_dist	float	Limit of Mahalanobis distance used for outliers detection	49.5	N/A

For twist measurement

Name	Type	Description	Default	Range
twist_additional_delay	float	Additional delay time for twist [s]	0	N/A
twist_smoothing_steps	integer	A value for smoothing steps	2	N/A
twist_gate_dist	float	Limit of Mahalanobis distance used for outliers detection	46.1	N/A

For process noise

Name	Type	Description	Default	Range
proc_stddev_vx_c	float	Standard deviation of process noise in time differentiation expression of linear velocity x, noise for d_vx = 0	10	N/A
proc_stddev_wz_c	float	Standard deviation of process noise in time differentiation expression of angular velocity z, noise for d_wz = 0	5	N/A
proc_stddev_yaw_c	float	Standard deviation of process noise in time differentiation expression of yaw, noise for d_yaw = omega	0.005	N/A

note: process noise for positions x & y are calculated automatically from nonlinear dynamics.

Simple 1D Filter Parameters

Name	Type	Description	Default	Range
z_filter_proc_dev	float	Simple1DFilter - Z filter process deviation	1	N/A
roll_filter_proc_dev	float	Simple1DFilter - Roll filter process deviation	0.1	N/A
pitch_filter_proc_dev	float	Simple1DFilter - Pitch filter process deviation	0.1	N/A

For diagnostics

Name	Type	Description	Default	Range
pose_no_update_count_threshold_warn	integer	The threshold at which a WARN state is triggered due to the Pose Topic update not happening continuously for a certain number of times	50	N/A
pose_no_update_count_threshold_error	integer	The threshold at which an ERROR state is triggered due to the Pose Topic update not happening continuously for a certain number of times	100	N/A
twist_no_update_count_threshold_warn	integer	The threshold at which a WARN state is triggered due to the Twist Topic update not happening continuously for a certain number of times	50	N/A
twist_no_update_count_threshold_error	integer	The threshold at which an ERROR state is triggered due to the Twist Topic update not happening continuously for a certain number of times	100	N/A
ellipse_scale	float	The scale factor to apply the error ellipse size	3	N/A
error_ellipse_size	float	The long axis size of the error ellipse to trigger a ERROR state	1.5	N/A
warn_ellipse_size	float	The long axis size of the error ellipse to trigger a WARN state	1.2	N/A
error_ellipse_size_lateral_direction	float	The lateral direction size of the error ellipse to trigger a ERROR state	0.3	N/A
warn_ellipse_size_lateral_direction	float	The lateral direction size of the error ellipse to trigger a WARN state	0.25	N/A

Misc

Name	Type	Description	Default	Range
threshold_observable_velocity_mps	float	Minimum value for velocity that will be used for EKF. Mainly used for dead zone in velocity sensor [m/s] (0.0 means disabled)	0.0	N/A
pose_frame_id	string	Parent frame_id of EKF output pose	map	N/A

How to tune EKF parameters

0. Preliminaries

• Check header time in pose and twist message is set to sensor time appropriately, because time delay is calculated from this value. If it is difficult to set an appropriate time due to the timer synchronization problem, use twist_additional_delay and pose_additional_delay to correct the time.
• Check if the relation between measurement pose and twist is appropriate (whether the derivative of the pose has a similar value to twist). This discrepancy is caused mainly by unit error (such as confusing radian/degree) or bias noise, and it causes large estimation errors.

1. Tune sensor parameters

Set standard deviation for each sensor. The pose_measure_uncertainty_time is for the uncertainty of the header timestamp data. You can also tune a number of steps for smoothing for each observed sensor data by tuning *_smoothing_steps. Increasing the number will improve the smoothness of the estimation, but may have an adverse effect on the estimation performance.

• pose_measure_uncertainty_time
• pose_smoothing_steps
• twist_smoothing_steps

2. Tune process model parameters

• proc_stddev_vx_c : set to maximum linear acceleration
• proc_stddev_wz_c : set to maximum angular acceleration
• proc_stddev_yaw_c : This parameter describes the correlation between the yaw and yaw rate. A large value means the change in yaw does not correlate to the estimated yaw rate. If this is set to 0, it means the change in estimated yaw is equal to yaw rate. Usually, this should be set to 0.
• proc_stddev_yaw_bias_c : This parameter is the standard deviation for the rate of change in yaw bias. In most cases, yaw bias is constant, so it can be very small, but must be non-zero.

3. Tune gate parameters

EKF performs gating using Mahalanobis distance before updating by observation. The gate size is determined by the pose_gate_dist parameter and the twist_gate_dist. If the Mahalanobis distance is larger than this value, the observation is ignored.

This gating process is based on a statistical test using the chi-square distribution. As modeled, we assume that the Mahalanobis distance follows a chi-square distribution with 3 degrees of freedom for pose and 2 degrees of freedom for twist.

Currently, the accuracy of covariance estimation itself is not very good, so it is recommended to set the significance level to a very small value to reduce rejection due to false positives.

Significance level	Threshold for 2 dof	Threshold for 3 dof
\(10 ^ {-2}\)	9.21	11.3
\(10 ^ {-3}\)	13.8	16.3
\(10 ^ {-4}\)	18.4	21.1
\(10 ^ {-5}\)	23.0	25.9
\(10 ^ {-6}\)	27.6	30.7
\(10 ^ {-7}\)	32.2	35.4
\(10 ^ {-8}\)	36.8	40.1
\(10 ^ {-9}\)	41.4	44.8
\(10 ^ {-10}\)	46.1	49.5

Kalman Filter Model

kinematics model in update function

where, \(\theta_k\) represents the vehicle's heading angle, including the mounting angle bias. \(b_k\) is a correction term for the yaw bias, and it is modeled so that \((\theta_k+b_k)\) becomes the heading angle of the base_link. The pose_estimator is expected to publish the base_link in the map coordinate system. However, the yaw angle may be offset due to calibration errors. This model compensates this error and improves estimation accuracy.

time delay model

The measurement time delay is handled by an augmented state [1] (See, Section 7.3 FIXED-LAG SMOOTHING).

Note that, although the dimension gets larger since the analytical expansion can be applied based on the specific structures of the augmented states, the computational complexity does not significantly change.

Test Result with Autoware NDT

Diagnostics

The conditions that result in a WARN state

• The node is not in the activate state.
• The number of consecutive no measurement update via the Pose/Twist topic exceeds the pose_no_update_count_threshold_warn/twist_no_update_count_threshold_warn.
• The timestamp of the Pose/Twist topic is beyond the delay compensation range.
• The Pose/Twist topic is beyond the range of Mahalanobis distance for covariance estimation.
• The covariance ellipse is bigger than threshold warn_ellipse_size for long axis or warn_ellipse_size_lateral_direction for lateral_direction.

The conditions that result in an ERROR state

• The number of consecutive no measurement update via the Pose/Twist topic exceeds the pose_no_update_count_threshold_error/twist_no_update_count_threshold_error.
• The covariance ellipse is bigger than threshold error_ellipse_size for long axis or error_ellipse_size_lateral_direction for lateral_direction.

Known issues

• If multiple pose_estimators are used, the input to the EKF will include multiple yaw biases corresponding to each source. However, the current EKF assumes the existence of only one yaw bias. Therefore, yaw bias b_k in the current EKF state would not make any sense and cannot correctly handle these multiple yaw biases. Thus, future work includes introducing yaw bias for each sensor with yaw estimation.

reference

[1] Anderson, B. D. O., & Moore, J. B. (1979). Optimal filtering. Englewood Cliffs, NJ: Prentice-Hall.