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Merge pull request #842 from tier4/beta-to-tier4-main-sync
chore: sync beta branch beta/v0.12.0 with tier4/main
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cmake_minimum_required(VERSION 3.8) | ||
project(cluster_merger) | ||
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# find dependencies | ||
find_package(autoware_cmake REQUIRED) | ||
autoware_package() | ||
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# Targets | ||
ament_auto_add_library(cluster_merger_node_component SHARED | ||
src/cluster_merger/node.cpp | ||
) | ||
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rclcpp_components_register_node(cluster_merger_node_component | ||
PLUGIN "cluster_merger::ClusterMergerNode" | ||
EXECUTABLE cluster_merger_node) | ||
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if(BUILD_TESTING) | ||
list(APPEND AMENT_LINT_AUTO_EXCLUDE ament_cmake_uncrustify) | ||
find_package(ament_lint_auto REQUIRED) | ||
ament_lint_auto_find_test_dependencies() | ||
endif() | ||
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ament_auto_package( | ||
INSTALL_TO_SHARE | ||
launch | ||
) |
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# cluster merger | ||
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## Purpose | ||
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cluster_merger is a package for merging pointcloud clusters as detected objects with feature type. | ||
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## Inner-working / Algorithms | ||
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The clusters of merged topics are simply concatenated from clusters of input topics. | ||
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## Input / Output | ||
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### Input | ||
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| Name | Type | Description | | ||
| ---------------- | -------------------------------------------------------- | ------------------- | | ||
| `input/cluster0` | `tier4_perception_msgs::msg::DetectedObjectsWithFeature` | pointcloud clusters | | ||
| `input/cluster1` | `tier4_perception_msgs::msg::DetectedObjectsWithFeature` | pointcloud clusters | | ||
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### Output | ||
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| Name | Type | Description | | ||
| ----------------- | ----------------------------------------------------- | --------------- | | ||
| `output/clusters` | `autoware_auto_perception_msgs::msg::DetectedObjects` | merged clusters | | ||
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## Parameters | ||
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| Name | Type | Description | Default value | | ||
| :---------------- | :----- | :----------------------------------- | :------------ | | ||
| `output_frame_id` | string | The header frame_id of output topic. | base_link | | ||
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## Assumptions / Known limits | ||
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<!-- Write assumptions and limitations of your implementation. | ||
Example: | ||
This algorithm assumes obstacles are not moving, so if they rapidly move after the vehicle started to avoid them, it might collide with them. | ||
Also, this algorithm doesn't care about blind spots. In general, since too close obstacles aren't visible due to the sensing performance limit, please take enough margin to obstacles. | ||
--> | ||
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## (Optional) Error detection and handling | ||
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<!-- Write how to detect errors and how to recover from them. | ||
Example: | ||
This package can handle up to 20 obstacles. If more obstacles found, this node will give up and raise diagnostic errors. | ||
--> | ||
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## (Optional) Performance characterization | ||
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<!-- Write performance information like complexity. If it wouldn't be the bottleneck, not necessary. | ||
Example: | ||
### Complexity | ||
This algorithm is O(N). | ||
### Processing time | ||
... | ||
--> | ||
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## (Optional) References/External links | ||
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<!-- Write links you referred to when you implemented. | ||
Example: | ||
[1] {link_to_a_thesis} | ||
[2] {link_to_an_issue} | ||
--> | ||
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## (Optional) Future extensions / Unimplemented parts |
Oops, something went wrong.