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AR Tag Based Localizer#

ArTagBasedLocalizer is a vision-based localization node.

ar_tag_image

This node uses the ArUco library to detect AR-Tags from camera images and calculates and publishes the pose of the ego vehicle based on these detections. The positions and orientations of the AR-Tags are assumed to be written in the Lanelet2 format.

Inputs / Outputs#

ar_tag_based_localizer node#

Input#

Name Type Description
~/input/lanelet2_map autoware_map_msgs::msg::LaneletMapBin Data of lanelet2
~/input/image sensor_msgs::msg::Image Camera Image
~/input/camera_info sensor_msgs::msg::CameraInfo Camera Info
~/input/ekf_pose geometry_msgs::msg::PoseWithCovarianceStamped EKF Pose without IMU correction. It is used to validate detected AR tags by filtering out False Positives. Only if the EKF Pose and the AR tag-detected Pose are within a certain temporal and spatial range, the AR tag-detected Pose is considered valid and published.

Output#

Name Type Description
~/output/pose_with_covariance geometry_msgs::msg::PoseWithCovarianceStamped Estimated Pose
~/debug/result sensor_msgs::msg::Image [debug topic] Image in which marker detection results are superimposed on the input image
~/debug/marker visualization_msgs::msg::MarkerArray [debug topic] Loaded landmarks to visualize in Rviz as thin boards
/tf geometry_msgs::msg::TransformStamped [debug topic] TF from camera to detected tag
/diagnostics diagnostic_msgs::msg::DiagnosticArray Diagnostics outputs

Parameters#

Name Type Description Default Range
marker_size float marker_size 0.6 N/A
target_tag_ids array target_tag_ids ['0','1','2','3','4','5','6'] N/A
base_covariance array base_covariance [0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 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, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02] N/A
distance_threshold float distance_threshold(m) 13.0 N/A
consider_orientation boolean consider_orientation false N/A
detection_mode string detection_mode select from [DM_NORMAL, DM_FAST, DM_VIDEO_FAST] DM_NORMAL N/A
min_marker_size float min_marker_size 0.02 N/A
ekf_time_tolerance float ekf_time_tolerance(sec) 5.0 N/A
ekf_position_tolerance float ekf_position_tolerance(m) 10.0 N/A

How to launch#

When launching Autoware, set artag for pose_source.

ros2 launch autoware_launch ... \
    pose_source:=artag \
    ...

Rosbag#

Sample rosbag and map (AWSIM data)#

This data is simulated data created by AWSIM. Essentially, AR tag-based self-localization is not intended for such public road driving, but for driving in a smaller area, so the max driving speed is set at 15 km/h.

It is a known problem that the timing of when each AR tag begins to be detected can cause significant changes in estimation.

sample_result_in_awsim

Sample rosbag and map (Real world data)#

Please remap the topic names and play it.

ros2 bag play /path/to/ar_tag_based_localizer_sample_bag/ -r 0.5 -s sqlite3 \
     --remap /sensing/camera/front/image:=/sensing/camera/traffic_light/image_raw \
             /sensing/camera/front/image/info:=/sensing/camera/traffic_light/camera_info

This dataset contains issues such as missing IMU data, and overall the accuracy is low. Even when running AR tag-based self-localization, significant difference from the true trajectory can be observed.

The image below shows the trajectory when the sample is executed and plotted.

sample_result

The pull request video below should also be helpful.

https://github.com/autowarefoundation/autoware.universe/pull/4347#issuecomment-1663155248

Principle#

principle