Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix(image_projection_based_fusion): segmentation pointcloud fusion param update #7858

Merged
merged 4 commits into from
Jul 5, 2024
Merged

fix(image_projection_based_fusion): segmentation pointcloud fusion param update #7858

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
c5d5fa3
fix: update param declare
badai-nguyen Jul 5, 2024
cb865a0
chore: remove unnecessary params
badai-nguyen Jul 5, 2024
9a3607b
fix: pre-commit
badai-nguyen Jul 5, 2024
7e13702
fix: cspell error
badai-nguyen Jul 5, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
53 changes: 19 additions & 34 deletions perception/image_projection_based_fusion/config/segmentation_pointcloud_fusion.param.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,39 +1,24 @@
/**:
ros__parameters:
# if the semantic label is applied for pointcloud filtering

filter_semantic_label_target:
[
true, # road
true, # sidewalk
true, # building
true, # wall
true, # fence
true, # pole
true, # traffic_light
true, # traffic_sign
true, # vegetation
true, # terrain
true, # sky
false, # person
false, # ride
false, # car
false, # truck
false, # bus
false, # train
false, # motorcycle
false, # bicycle
false, # others
]
# the maximum distance of pointcloud to be applied filter,
# this is selected based on semantic segmentation model accuracy,
# calibration accuracy and unknown reaction distance
filter_distance_threshold: 60.0
UNKNOWN: false
BUILDING: true
WALL: true
OBSTACLE: false
TRAFFIC_LIGHT: false
TRAFFIC_SIGN: false
PERSON: false
VEHICLE: false
BIKE: false
ROAD: true
SIDEWALK: false
ROAD_PAINT: false
CURBSTONE: false
CROSSWALK: false
VEGETATION: true
SKY: false

# debug
debug_mode: false
filter_scope_min_x: -100.0
filter_scope_max_x: 100.0
filter_scope_min_y: -100.0
filter_scope_max_y: 100.0
filter_scope_min_z: -100.0
filter_scope_max_z: 100.0
# the maximum distance of pointcloud to be applied filter
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I can not get what distance means until reading the code.
It means maximum z depth, so how about change its explanation?

Suggested change
# the maximum distance of pointcloud to be applied filter
# the maximum depth of pointcloud to be applied filter

filter_distance_threshold: 60.0
4 changes: 1 addition & 3 deletions perception/image_projection_based_fusion/docs/segmentation-pointcloud-fusion.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,9 +32,7 @@ The node `segmentation_pointcloud_fusion` is a package for filtering pointcloud

### Core Parameters

| Name | Type | Description |
| ------------- | ---- | ------------------------ |
| `rois_number` | int | the number of input rois |
{{ json_to_markdown("perception/image_projection_based_fusion/schema/segmentation_pointcloud_fusion.schema.json") }}

## Assumptions / Known limits

Expand Down
9 changes: 8 additions & 1 deletion ...ased_fusion/include/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
#include <image_projection_based_fusion/utils/utils.hpp>

#include <string>
#include <utility>
#include <vector>

#if __has_include(<cv_bridge/cv_bridge.hpp>)
Expand All @@ -36,7 +37,13 @@ class SegmentPointCloudFusionNode : public FusionNode<PointCloud2, PointCloud2,
rclcpp::Publisher<PointCloud2>::SharedPtr pub_pointcloud_ptr_;
std::vector<bool> filter_semantic_label_target_;
float filter_distance_threshold_;
/* data */
// declare list of semantic label target, depend on trained data of yolox segmentation model
std::vector<std::pair<std::string, bool>> filter_semantic_label_target_list_ = {
{"UNKNOWN", false}, {"BUILDING", false}, {"WALL", false}, {"OBSTACLE", false},
{"TRAFFIC_LIGHT", false}, {"TRAFFIC_SIGN", false}, {"PERSON", false}, {"VEHICLE", false},
{"BIKE", false}, {"ROAD", false}, {"SIDEWALK", false}, {"ROAD_PAINT", false},
{"CURBSTONE", false}, {"CROSSWALK", false}, {"VEGETATION", false}, {"SKY", false}};

public:
explicit SegmentPointCloudFusionNode(const rclcpp::NodeOptions & options);

Expand Down
134 changes: 134 additions & 0 deletions perception/image_projection_based_fusion/schema/segmentation_pointcloud_fusion.schema.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,134 @@
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "Parameters for Segmentation Pointcloud Fusion Node",
"type": "object",
"definitions": {
"segmentation_pointcloud_fusion": {
"type": "object",
"properties": {
"filter_semantic_label_target": {
"type": "object",
"properties": {
"UNKNOWN": {
"type": "boolean",
"description": "If true, UNKNOWN class of semantic will be filtered.",
"default": false
},
"BUILDING": {
"type": "boolean",
"description": "If true, BUILDING class of semantic will be filtered.",
"default": true
},
"WALL": {
"type": "boolean",
"description": "If true, WALL class of semantic will be filtered.",
"default": true
},
"OBSTACLE": {
"type": "boolean",
"description": "If true, OBSTACLE class of semantic will be filtered.",
"default": false
},
"TRAFFIC_LIGHT": {
"type": "boolean",
"description": "If true, TRAFFIC_LIGHT class of semantic will be filtered.",
"default": false
},
"TRAFFIC_SIGN": {
"type": "boolean",
"description": "If true, TRAFFIC_SIGN class of semantic will be filtered.",
"default": false
},
"PERSON": {
"type": "boolean",
"description": "If true, PERSON class of semantic will be filtered.",
"default": false
},
"VEHICLE": {
"type": "boolean",
"description": "If true, VEHICLE class of semantic will be filtered.",
"default": false
},
"BIKE": {
"type": "boolean",
"description": "If true, BIKE class of semantic will be filtered.",
"default": false
},
"ROAD": {
"type": "boolean",
"description": "If true, ROAD class of semantic will be filtered.",
"default": true
},
"SIDEWALK": {
"type": "boolean",
"description": "If true, SIDEWALK class of semantic will be filtered.",
"default": false
},
"ROAD_PAINT": {
"type": "boolean",
"description": "If true, ROAD_PAINT class of semantic will be filtered.",
"default": false
},
"CURBSTONE": {
"type": "boolean",
"description": "If true, CURBSTONE class of semantic will be filtered.",
"default": false
},
"CROSSWALK": {
"type": "boolean",
"description": "If true, CROSSWALK class of semantic will be filtered.",
"default": false
},
"VEGETATION": {
"type": "boolean",
"description": "If true, VEGETATION class of semantic will be filtered.",
"default": true
},
"SKY": {
"type": "boolean",
"description": "If true, SKY class of semantic will be filtered.",
"default": false
}
},
"required": [
"UNKNOWN",
"BUILDING",
"WALL",
"OBSTACLE",
"TRAFFIC_LIGHT",
"TRAFFIC_SIGN",
"PERSON",
"VEHICLE",
"BIKE",
"ROAD",
"SIDEWALK",
"ROAD_PAINT",
"CURBSTONE",
"CROSSWALK",
"VEGETATION",
"SKY"
]
},
"filter_distance_threshold": {
"type": "number",
"description": "A maximum distance of pointcloud to apply filter [m].",
"default": 60.0,
"minimum": 0.0
}
},
"required": ["filter_semantic_label_target", "filter_distance_threshold"]
}
},
"properties": {
"/**": {
"type": "object",
"properties": {
"ros__parameters": {
"$ref": "#/definitions/segmentation_pointcloud_fusion"
}
},
"required": ["ros__parameters"]
}
},
"required": ["/**"]
}
13 changes: 9 additions & 4 deletions perception/image_projection_based_fusion/src/segmentation_pointcloud_fusion/node.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,8 +31,13 @@ SegmentPointCloudFusionNode::SegmentPointCloudFusionNode(const rclcpp::NodeOptio
: FusionNode<PointCloud2, PointCloud2, Image>("segmentation_pointcloud_fusion", options)
{
filter_distance_threshold_ = declare_parameter<float>("filter_distance_threshold");
filter_semantic_label_target_ =
declare_parameter<std::vector<bool>>("filter_semantic_label_target");
for (auto & item : filter_semantic_label_target_list_) {
item.second = declare_parameter<bool>("filter_semantic_label_target." + item.first);
}
for (const auto & item : filter_semantic_label_target_list_) {
RCLCPP_INFO(
this->get_logger(), "filter_semantic_label_target: %s %d", item.first.c_str(), item.second);
}
}

void SegmentPointCloudFusionNode::preprocess(__attribute__((unused)) PointCloud2 & pointcloud_msg)
Expand Down Expand Up @@ -129,12 +134,12 @@ void SegmentPointCloudFusionNode::fuseOnSingleImage(
// skip filtering pointcloud where semantic id out of the defined list
uint8_t semantic_id = mask.at<uint8_t>(
static_cast<uint16_t>(projected_point.y()), static_cast<uint16_t>(projected_point.x()));
if (static_cast<size_t>(semantic_id) >= filter_semantic_label_target_.size()) {
if (static_cast<size_t>(semantic_id) >= filter_semantic_label_target_list_.size()) {
copyPointCloud(
input_pointcloud_msg, point_step, global_offset, output_cloud, output_pointcloud_size);
continue;
}
if (!filter_semantic_label_target_.at(semantic_id)) {
if (!filter_semantic_label_target_list_.at(semantic_id).second) {
copyPointCloud(
input_pointcloud_msg, point_step, global_offset, output_cloud, output_pointcloud_size);
}
Expand Down
Loading