### ekf config file ### ekf_filter_node: ros__parameters: # The frequency, in Hz, at which the filter will output a position estimate. Note that the filter will not begin # computation until it receives at least one message from one of the inputs. It will then run continuously at the # frequency specified here, regardless of whether it receives more measurements. Defaults to 30 if unspecified. frequency: 30.0 # The period, in seconds, after which we consider a sensor to have timed out. In this event, we carry out a predict # cycle on the EKF without correcting it. This parameter can be thought of as the minimum frequency with which the # filter will generate new output. Defaults to 1 / frequency if not specified. sensor_timeout: 0.1 # ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is # set to true, no 3D information will be used in your state estimate. Use this if you are operating in a planar # environment and want to ignore the effect of small variations in the ground plane that might otherwise be detected # by, for example, an IMU. Defaults to false if unspecified. two_d_mode: true # Use this parameter to provide an offset to the transform generated by ekf_localization_node. This can be used for # future dating the transform, which is required for interaction with some other packages. Defaults to 0.0 if # unspecified. transform_time_offset: 0.0 # Use this parameter to provide specify how long the tf listener should wait for a transform to become available. # Defaults to 0.0 if unspecified. transform_timeout: 0.0 # If you're having trouble, try setting this to true, and then echo the /diagnostics_agg topic to see if the node is # unhappy with any settings or data. print_diagnostics: true # Debug settings. Not for the faint of heart. Outputs a ludicrous amount of information to the file specified by # debug_out_file. I hope you like matrices! Please note that setting this to true will have strongly deleterious # effects on the performance of the node. Defaults to false if unspecified. debug: false # Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path. debug_out_file: /path/to/debug/file.txt # Whether we'll allow old measurements to cause a re-publication of the updated state permit_corrected_publication: false # Whether to publish the acceleration state. Defaults to false if unspecified. publish_acceleration: false # Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified. publish_tf: true # REP-105 (http://www.ros.org/reps/rep-0105.html) specifies four principal coordinate frames: base_link, odom, map, and # earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames. # The robot's position in the odom frame will drift over time, but is accurate in the short term and should be # continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom # frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your # robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based # localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame. # ekf_localization_node and ukf_localization_node are not concerned with the earth frame. # Here is how to use the following settings: # 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system. # 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of # odom_frame. # 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set # "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes. # 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates # from landmark observations) then: # 3a. Set your "world_frame" to your map_frame value # 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state # estimation node from robot_localization! However, that instance should *not* fuse the global data. # map_frame: odom # Defaults to "map" if unspecified odom_frame: odom # Defaults to "odom" if unspecified base_link_frame: base_link # Defaults to "base_link" if unspecified # world_frame: odom # Defaults to the value of odom_frame if unspecified # The filter accepts an arbitrary number of inputs from each input message type (nav_msgs/Odometry, # geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped, # sensor_msgs/Imu). To add an input, simply append the next number in the sequence to its "base" name, e.g., odom0, # odom1, twist0, twist1, imu0, imu1, imu2, etc. The value should be the topic name. These parameters obviously have no # default values, and must be specified. odom0: /go2/odom # Each sensor reading updates some or all of the filter's state. These options give you greater control over which # values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only # want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the # values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types # do not provide some of the state variables estimated by the filter. For example, a TwistWithCovarianceStamped message # has no pose information, so the first six values would be meaningless in that case. Each vector defaults to all false # if unspecified, effectively making this parameter required for each sensor. odom0_config: [true, true, true, true, true, true, true, true, true, true, true, true, false, false, false] # If you have high-frequency data or are running with a low frequency parameter value, then you may want to increase # the size of the subscription queue so that more measurements are fused. odom0_queue_size: 2 # [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result # of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's # algorithm. odom0_nodelay: false # [ADVANCED] When measuring one pose variable with two sensors, a situation can arise in which both sensors under- # report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they # arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also # measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't # always feasible, and so we expose the differential parameter. When differential mode is enabled, all absolute pose # data is converted to velocity data by differentiating the absolute pose measurements. These velocities are then # integrated as usual. NOTE: this only applies to sensors that provide pose measurements; setting differential to true # for twist measurements has no effect. odom0_differential: false # [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point" # for all future measurements. While you can achieve the same effect with the differential parameter, the key # difference is that the relative parameter doesn't cause the measurement to be converted to a velocity before # integrating it. If you simply want your measurements to start at 0 for a given sensor, set this to true. odom0_relative: true # [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to # control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to # numeric_limits::max() if unspecified. It is strongly recommended that these parameters be removed if not # required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation. # For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying # the thresholds. odom0_pose_rejection_threshold: 5.0 odom0_twist_rejection_threshold: 1.0 # imu0: /utlidar/imu # imu0_config: [false, false, false, # true, true, true, # false, false, false, # true, true, true, # true, true, true] # imu0_nodelay: false # imu0_differential: false # imu0_relative: true # imu0_queue_size: 5 # imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names # imu0_twist_rejection_threshold: 0.8 # # imu0_linear_acceleration_rejection_threshold: 0.8 # # [ADVANCED] Some IMUs automatically remove acceleration due to gravity, and others don't. If yours doesn't, please set # this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame. imu0_remove_gravitational_acceleration: true # [ADVANCED] The EKF and UKF models follow a standard predict/correct cycle. During prediction, if there is no # acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During # correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be # problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When # this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially # noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance # for the velocity variable in question, or decrease the variance of the variable in question in the measurement # itself. In addition, users can also take advantage of the control command being issued to the robot at the time we # make the prediction. If control is used, it will get converted into an acceleration term, which will be used during # predicition. Note that if an acceleration measurement for the variable in question is available from one of the # inputs, the control term will be ignored. # Whether or not we use the control input during predicition. Defaults to false. use_control: true # Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to # false. stamped_control: false # The last issued control command will be used in prediction for this period. Defaults to 0.2. control_timeout: 0.2 # Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw. control_config: [true, false, false, false, false, true] # Places limits on how large the acceleration term will be. Should match your robot's kinematics. acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4] # Acceleration and deceleration limits are not always the same for robots. deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5] # If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these # gains acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9] # If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these # gains deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0] # [ADVANCED] The process noise covariance matrix can be difficult to tune, and can vary for each application, so it is # exposed as a configuration parameter. This matrix represents the noise we add to the total error after each # prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be. # However, if users find that a given variable is slow to converge, one approach is to increase the # process_noise_covariance diagonal value for the variable in question, which will cause the filter's predicted error # to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are # ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below if # unspecified. process_noise_covariance: [0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015] # [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal # value (variance) to a large value will result in rapid convergence for initial measurements of the variable in # question. Users should take care not to use large values for variables that will not be measured directly. The values # are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below #if unspecified. initial_estimate_covariance: [1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9]