pointcloud_preprocessor#
Purpose#
The pointcloud_preprocessor is a package that includes the following filters:
- removing outlier points
- cropping
- concatenating pointclouds
- correcting distortion
- downsampling
Inner-workings / Algorithms#
Detail description of each filter's algorithm is in the following links.
| Filter Name | Description | Detail |
|---|---|---|
| concatenate_data | subscribe multiple pointclouds and concatenate them into a pointcloud | link |
| crop_box_filter | remove points within a given box | link |
| distortion_corrector | compensate pointcloud distortion caused by ego vehicle's movement during 1 scan | link |
| downsample_filter | downsampling input pointcloud | link |
| outlier_filter | remove points caused by hardware problems, rain drops and small insects as a noise | link |
| passthrough_filter | remove points on the outside of a range in given field (e.g. x, y, z, intensity) | link |
| pointcloud_accumulator | accumulate pointclouds for a given amount of time | link |
| vector_map_filter | remove points on the outside of lane by using vector map | link |
Inputs / Outputs#
Input#
| Name | Type | Description |
|---|---|---|
~/input/points |
sensor_msgs::msg::PointCloud2 |
reference points |
~/input/indices |
pcl_msgs::msg::Indices |
reference indices |
Output#
| Name | Type | Description |
|---|---|---|
~/output/points |
sensor_msgs::msg::PointCloud2 |
filtered points |
Parameters#
Node Parameters#
| Name | Type | Default Value | Description |
|---|---|---|---|
input_frame |
string | " " | input frame id |
output_frame |
string | " " | output frame id |
max_queue_size |
int | 5 | max queue size of input/output topics |
use_indices |
bool | false | flag to use pointcloud indices |
latched_indices |
bool | false | flag to latch pointcloud indices |
approximate_sync |
bool | false | flag to use approximate sync option |
Assumptions / Known limits#
pointcloud_preprocessor::Filter is implemented based on pcl_perception [1] because of this issue.
(Optional) Error detection and handling#
(Optional) Performance characterization#
References/External links#
[1] https://github.com/ros-perception/perception_pcl/blob/ros2/pcl_ros/src/pcl_ros/filters/filter.cpp