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 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).

Launch#

The ekf_localizer starts with the default parameters with the following command.

roslaunch ekf_localizer ekf_localizer.launch

The parameters and input topic names can be set in the ekf_localizer.launch file.

Node#

Subscribed Topics#

measured_pose_with_covariance (geometry_msgs/PoseWithCovarianceStamped)

Input pose source with the measurement covariance matrix.

measured_twist_with_covariance (geometry_msgs/TwistWithCovarianceStamped)

Input twist source with the measurement covariance matrix.

initialpose (geometry_msgs/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#

ekf_odom (nav_msgs/Odometry)

Estimated odometry.

ekf_pose (geometry_msgs/PoseStamped)

Estimated pose.

ekf_pose_with_covariance (geometry_msgs/PoseWithCovarianceStamped)

Estimated pose with covariance.

ekf_biased_pose (geometry_msgs/PoseStamped)

Estimated pose including the yaw bias

ekf_biased_pose_with_covariance (geometry_msgs/PoseWithCovarianceStamped)

Estimated pose with covariance including the yaw bias

ekf_twist (geometry_msgs/TwistStamped)

Estimated twist.

ekf_twist_with_covariance (geometry_msgs/TwistWithCovarianceStamped)

The estimated twist with covariance.

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 value
show_debug_info	bool	Flag to display debug info	false
predict_frequency	double	Frequency for filtering and publishing [Hz]	50.0
tf_rate	double	Frequency for tf broadcasting [Hz]	10.0
extend_state_step	int	Max delay step which can be dealt with in EKF. Large number increases computational cost.	50
enable_yaw_bias_estimation	bool	Flag to enable yaw bias estimation	true

For pose measurement#

Name	Type	Description	Default value
pose_additional_delay	double	Additional delay time for pose measurement [s]	0.0
pose_measure_uncertainty_time	double	Measured time uncertainty used for covariance calculation [s]	0.01
pose_smoothing_steps	int	A value for smoothing steps	5
pose_gate_dist	double	Limit of Mahalanobis distance used for outliers detection	10000.0

For twist measurement#

Name	Type	Description	Default value
twist_additional_delay	double	Additional delay time for twist [s]	0.0
twist_smoothing_steps	int	A value for smoothing steps	2
twist_gate_dist	double	Limit of Mahalanobis distance used for outliers detection	10000.0

For process noise#

Name	Type	Description	Default value
proc_stddev_vx_c	double	Standard deviation of process noise in time differentiation expression of linear velocity x, noise for d_vx = 0	2.0
proc_stddev_wz_c	double	Standard deviation of process noise in time differentiation expression of angular velocity z, noise for d_wz = 0	0.2
proc_stddev_yaw_c	double	Standard deviation of process noise in time differentiation expression of yaw, noise for d_yaw = omega	0.005
proc_stddev_yaw_bias_c	double	Standard deviation of process noise in time differentiation expression of yaw_bias, noise for d_yaw_bias = 0	0.001

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

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 yawrate. A large value means the change in yaw does not correlate to the estimated yawrate. If this is set to 0, it means the change in estimated yaw is equal to yawrate. 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.

Kalman Filter Model#

kinematics model in update function#

where b_k is the yawbias.

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#

Known issues#

In the presence of multiple inputs with yaw estimation, yaw bias b_k in the current EKF state would not make any sense, since it is intended to capture the extrinsic parameter's calibration error of a sensor. 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.