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fix(euclidean_cluster): fix euclidean cluster params #7662

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merged 9 commits into from
Jul 1, 2024
Merged

fix(euclidean_cluster): fix euclidean cluster params #7662

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bf0b1f6
fix(euclidean_cluster): fix max and min cluster size
beginningfan Jun 21, 2024
1f9f177
Merge branch 'autowarefoundation:main' into fix/euclidean-cluster-params
beginningfan Jun 21, 2024
857d378
Merge branch 'autowarefoundation:main' into fix/euclidean-cluster-params
beginningfan Jun 21, 2024
4c732e5
fix(gnss_poser): fix a typo
beginningfan Jun 24, 2024
51dc439
Merge branch 'autowarefoundation:main' into fix/euclidean-cluster-params
beginningfan Jun 24, 2024
3ae673b
fix(euclidean_cluster): fix min cluster size
beginningfan Jun 24, 2024
8810607
style(pre-commit): autofix
pre-commit-ci[bot] Jun 24, 2024
dca31f0
Merge branch 'autowarefoundation:main' into fix/euclidean-cluster-params
beginningfan Jun 28, 2024
69483aa
Merge branch 'autowarefoundation:main' into fix/euclidean-cluster-params
beginningfan Jul 1, 2024
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6 changes: 5 additions & 1 deletion perception/euclidean_cluster/lib/voxel_grid_based_euclidean_cluster.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -106,24 +106,28 @@
temporary_cluster.height = pointcloud_msg->height;
temporary_cluster.fields = pointcloud_msg->fields;
temporary_cluster.point_step = point_step;
temporary_cluster.data.resize(max_cluster_size_ * point_step);
temporary_cluster.data.resize(cluster.indices.size() * point_step);
clusters_data_size.push_back(0);
}

// create vector of point cloud cluster. vector index is voxel grid index.
for (size_t i = 0; i < pointcloud->points.size(); ++i) {
const auto & point = pointcloud->points.at(i);
const int index =
voxel_grid_.getCentroidIndexAt(voxel_grid_.getGridCoordinates(point.x, point.y, point.z));
if (map.find(index) != map.end()) {
auto & cluster_data_size = clusters_data_size.at(map[index]);
if (cluster_data_size + point_step > std::size_t(max_cluster_size_ * point_step)) {
continue;
}
std::memcpy(
&temporary_clusters.at(map[index]).data[cluster_data_size],
&pointcloud_msg->data[i * point_step], point_step);
cluster_data_size += point_step;
if (cluster_data_size == temporary_clusters.at(map[index]).data.size()) {
temporary_clusters.at(map[index])
.data.resize(temporary_clusters.at(map[index]).data.size() * 2);
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@badai-nguyen badai-nguyen Jun 26, 2024 •
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using the maximum value to allocate space will cause excessive CPU and memory usage.

@beginningfan I agree with you.
However, I think decide that the max size of output cluster (cluster_data_size) to be doubled of temporary_cluster is not reasonable. (runtime_error might be occurred here for some high dense pointcloud)

Suggested change
.data.resize(temporary_clusters.at(map[index]).data.size() * 2);
.data.resize(max_cluster_size_ * point_step);

Or we can increment cluster_data_size and check it repeatedly.

    if (cluster_data_size >= temporary_clusters.at(map[index]).data.size()) {
        temporary_clusters.at(map[index]).data.resize(cluster_data_size + point_step * 10);
      }
      std::memcpy(
        &temporary_clusters.at(map[index]).data[cluster_data_size],
        &pointcloud_msg->data[i * point_step], point_step);
      cluster_data_size += point_step;
    }

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Here is a for loop that reallocates twice the space every time the space is full. When the space is full, it will twice the space again.I have tested using a ouster64, and the dense pointcloud is sometimes 50 times larger than the downsampled one. Even so, enough space can be allocated quickly.
If you use max_cluster_size_, it might as well just use the original code. If we add 10 points each time, this will result in multiple copies of the vector data.

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@beginningfan I understood. I have checked and it worked well. Thank you for your improvement.

}

Check warning on line 130 in perception/euclidean_cluster/lib/voxel_grid_based_euclidean_cluster.cpp

View check run for this annotation

CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

❌ Getting worse: Complex Method 

VoxelGridBasedEuclideanCluster::cluster increases in cyclomatic complexity from 12 to 13, threshold = 9. This function has many conditional statements (e.g. if, for, while), leading to lower code health. Avoid adding more conditionals and code to it without refactoring.
}
}

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