add README of euclidean_cluster (#614)

* add README of euclidean_cluster

* add period

Co-authored-by: Kazuki Miyahara <kmiya@outlook.com>

* update README

Co-authored-by: Kazuki Miyahara <kmiya@outlook.com>

perception/euclidean_cluster/README.md

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+# euclidean_cluster
+
+## Purpose
+
+euclidean_cluster is a package for clustering points into smaller parts to classify objects.
+
+This package has two clustering methods: `euclidean_cluster` and `voxel_grid_based_euclidean_cluster`.
+
+## Inner-workings / Algorithms
+
+### euclidean_cluster
+
+`pcl::EuclideanClusterExtraction` is applied to points. See [official document](https://pcl.readthedocs.io/en/latest/cluster_extraction.html) for details.
+
+### voxel_grid_based_euclidean_cluster
+
+1. A centroid in each voxel is calculated by `pcl::VoxelGrid`.
+2. The centroids are clustered by `pcl::EuclideanClusterExtraction`.
+3. The input points are clustered based on the clustered centroids.
+
+## Inputs / Outputs
+
+### Input
+
+| Name    | Type                            | Description      |
+| ------- | ------------------------------- | ---------------- |
+| `input` | `sensor_msgs::msg::PointCloud2` | input pointcloud |
+
+### Output
+
+| Name             | Type                                                        | Description                                  |
+| ---------------- | ----------------------------------------------------------- | -------------------------------------------- |
+| `output`         | `autoware_perception_msgs::msg::DetectedObjectsWithFeature` | cluster pointcloud                           |
+| `debug/clusters` | `sensor_msgs::msg::PointCloud2`                             | colored cluster pointcloud for visualization |
+
+## Parameters
+
+### Core Parameters
+
+#### euclidean_cluster
+
+| Name               | Type  | Description                                                                                  |
+| ------------------ | ----- | -------------------------------------------------------------------------------------------- |
+| `use_height`       | bool  | use point.z for clustering                                                                   |
+| `min_cluster_size` | int   | the minimum number of points that a cluster needs to contain in order to be considered valid |
+| `max_cluster_size` | int   | the maximum number of points that a cluster needs to contain in order to be considered valid |
+| `tolerance`        | float | the spatial cluster tolerance as a measure in the L2 Euclidean space                         |
+
+#### voxel_grid_based_euclidean_cluster
+
+| Name                          | Type  | Description                                                                                  |
+| ----------------------------- | ----- | -------------------------------------------------------------------------------------------- |
+| `use_height`                  | bool  | use point.z for clustering                                                                   |
+| `min_cluster_size`            | int   | the minimum number of points that a cluster needs to contain in order to be considered valid |
+| `max_cluster_size`            | int   | the maximum number of points that a cluster needs to contain in order to be considered valid |
+| `tolerance`                   | float | the spatial cluster tolerance as a measure in the L2 Euclidean space                         |
+| `voxel_leaf_size`             | float | the voxel leaf size of x and y                                                               |
+| `min_points_number_per_voxel` | int   | the minimum number of points for a voxel                                                     |
+
+## Assumptions / Known limits
+
+<!-- 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.
+-->
+
+## (Optional) Error detection and handling
+
+<!-- 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.
+-->
+
+## (Optional) Performance characterization
+
+<!-- Write performance information like complexity. If it wouldn't be the bottleneck, not necessary.
+
+Example:
+  ### Complexity
+
+  This algorithm is O(N).
+
+  ### Processing time
+
+  ...
+-->
+
+## (Optional) References/External links
+
+<!-- Write links you referred to when you implemented.
+
+Example:
+  [1] {link_to_a_thesis}
+  [2] {link_to_an_issue}
+-->
+
+## (Optional) Future extensions / Unimplemented parts
+
+The `use_height` option of `voxel_grid_based_euclidean_cluster` isn't implemented yet.