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concatenate_data#

Purpose#

Many self-driving cars combine multiple LiDARs to expand the sensing range. Therefore, a function to combine a plurality of point clouds is required.

To combine multiple sensor data with a similar timestamp, the message_filters is often used in the ROS-based system, but this requires the assumption that all inputs can be received. Since safety must be strongly considered in autonomous driving, the point clouds concatenate node must be designed so that even if one sensor fails, the remaining sensor information can be output.

Inner-workings / Algorithms#

The figure below represents the reception time of each sensor data and how it is combined in the case.

concatenate_data_timing_chart

Inputs / Outputs#

Input#

Name Type Description
~/input/twist autoware_auto_vehicle_msgs::msg::VelocityReport The vehicle odometry is used to interpolate the timestamp of each sensor data

Output#

Name Type Description
~/output/points sensor_msgs::msg::Pointcloud2 concatenated point clouds

Parameters#

Name Type Default Value Description
input/points vector of string [] input topic names that type must be sensor_msgs::msg::Pointcloud2
input_frame string "" input frame id
output_frame string "" output frame id
max_queue_size int 5 max queue size of input/output topics

Core Parameters#

Name Type Default Value Description
timeout_sec double 0.1 tolerance of time to publish next pointcloud [s]
When this time limit is exceeded, the filter concatenates and publishes pointcloud, even if not all the point clouds are subscribed.

Assumptions / Known limits#

It is necessary to assume that the vehicle odometry value exists, the sensor data and odometry timestamp are correct, and the TF from base_link to sensor_frame is also correct.

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