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feat: add lidar centerpoint package #71

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feat: add lidar centerpoint package #71

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06faf54
Feature/porting centerpoint (#1658)
wep21 Oct 21, 2021
30ab88e
Fix centerpoint object qos (#1747)
wep21 Oct 21, 2021
45c2d7e
Fix package.xml (#2056)
Sep 10, 2021
4b008c8
Detection by tracker (#1910)
yukkysaito Sep 29, 2021
eea2d95
fix for createQuaternionFromRPY/Yaw (#2154)
takayuki5168 Oct 1, 2021
c1538be
Change formatter to clang-format and black (#2332)
kenji-miyake Nov 2, 2021
750c006
Add COLCON_IGNORE (#500)
kenji-miyake Nov 4, 2021
d1c45c5
port lidar_centerpoint (#527)
yukke42 Nov 10, 2021
83ffc0e
Sync .auto branch with the latest branch in internal repository (#691)
wep21 Nov 18, 2021
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port lidar_centerpoint (#527) 
* delete COLCON_IGNORE

* change autoware_perception_msgs to autoware_auto_perception_msgs

* use existence_probability

* use orientation_availability

* about about existence_probability
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@yukke42 @YoheiMishina
yukke42 authored and YoheiMishina committed Dec 1, 2021
commit d1c45c5a14fe50e109c3b701d88ac39a4aa4af56
Empty file removed perception/lidar_centerpoint/COLCON_IGNORE
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2 changes: 2 additions & 0 deletions perception/lidar_centerpoint/README.md
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Expand Up @@ -4,6 +4,8 @@ This is a 3D object detection implementation of CenterPoint supporting TensorRT

The models are trained with [OpenPCDet](https://github.com/tier4/OpenPCDet)

The object.existence_probability is stored the value of classification confidence of DNN, not probability.

## Parameters

### Input Topics
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8 changes: 5 additions & 3 deletions perception/lidar_centerpoint/include/lidar_centerpoint/node.hpp
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Expand Up @@ -20,7 +20,10 @@
#include <pointcloud_densification.hpp>
#include <rclcpp/rclcpp.hpp>

#include <autoware_perception_msgs/msg/dynamic_object_with_feature_array.hpp>
#include <autoware_auto_perception_msgs/msg/detected_object_kinematics.hpp>
#include <autoware_auto_perception_msgs/msg/detected_objects.hpp>
#include <autoware_auto_perception_msgs/msg/object_classification.hpp>
#include <autoware_auto_perception_msgs/msg/shape.hpp>
#include <sensor_msgs/msg/point_cloud2.hpp>

#include <memory>
Expand All @@ -37,8 +40,7 @@ class LidarCenterPointNode : public rclcpp::Node
void pointCloudCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr input_pointcloud_msg);

rclcpp::Subscription<sensor_msgs::msg::PointCloud2>::SharedPtr pointcloud_sub_;
rclcpp::Publisher<autoware_perception_msgs::msg::DynamicObjectWithFeatureArray>::SharedPtr
objects_pub_;
rclcpp::Publisher<autoware_auto_perception_msgs::msg::DetectedObjects>::SharedPtr objects_pub_;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pointcloud_pub_;

float score_threshold_{0.0};
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2 changes: 1 addition & 1 deletion perception/lidar_centerpoint/package.xml
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Expand Up @@ -10,7 +10,7 @@

<buildtool_depend>ament_cmake_auto</buildtool_depend>

<depend>autoware_perception_msgs</depend>
<depend>autoware_auto_perception_msgs</depend>
<depend>autoware_utils</depend>
<depend>pcl_ros</depend>
<depend>rclcpp</depend>
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71 changes: 37 additions & 34 deletions perception/lidar_centerpoint/src/node.cpp
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Expand Up @@ -52,9 +52,8 @@ LidarCenterPointNode::LidarCenterPointNode(const rclcpp::NodeOptions & node_opti
pointcloud_sub_ = this->create_subscription<sensor_msgs::msg::PointCloud2>(
"~/input/pointcloud", rclcpp::SensorDataQoS{}.keep_last(1),
std::bind(&LidarCenterPointNode::pointCloudCallback, this, std::placeholders::_1));
objects_pub_ =
this->create_publisher<autoware_perception_msgs::msg::DynamicObjectWithFeatureArray>(
"~/output/objects", rclcpp::QoS{1});
objects_pub_ = this->create_publisher<autoware_auto_perception_msgs::msg::DetectedObjects>(
"~/output/objects", rclcpp::QoS{1});
pointcloud_pub_ = this->create_publisher<sensor_msgs::msg::PointCloud2>(
"~/debug/pointcloud_densification", rclcpp::SensorDataQoS{}.keep_last(1));
}
Expand All @@ -73,7 +72,7 @@ void LidarCenterPointNode::pointCloudCallback(
auto stacked_pointcloud_msg = densification_ptr_->stackPointCloud(*input_pointcloud_msg);
std::vector<float> boxes3d_vec = detector_ptr_->detect(stacked_pointcloud_msg);

autoware_perception_msgs::msg::DynamicObjectWithFeatureArray output_msg;
autoware_auto_perception_msgs::msg::DetectedObjects output_msg;
output_msg.header = input_pointcloud_msg->header;
for (size_t obj_i = 0; obj_i < boxes3d_vec.size() / Config::num_box_features; obj_i++) {
float score = boxes3d_vec[obj_i * Config::num_box_features + 0];
Expand All @@ -90,46 +89,50 @@ void LidarCenterPointNode::pointCloudCallback(
float h = boxes3d_vec[obj_i * Config::num_box_features + 7];
float yaw = boxes3d_vec[obj_i * Config::num_box_features + 8];

autoware_perception_msgs::msg::DynamicObjectWithFeature feature_obj;
autoware_perception_msgs::msg::Semantic semantic;
autoware_auto_perception_msgs::msg::DetectedObject obj;
// TODO(yukke42): the value of classification confidence of DNN, not probability.
obj.existence_probability = score;

autoware_auto_perception_msgs::msg::ObjectClassification classification;
classification.probability = 1.0f;
switch (class_id) {
case 0:
semantic.type = autoware_perception_msgs::msg::Semantic::CAR;
classification.label = autoware_auto_perception_msgs::msg::ObjectClassification::CAR;

// Note: object size is referred from multi_object_tracker
if ((w * l > 2.2 * 5.5) && (w * l <= 2.5 * 7.9)) {
classification.label = autoware_auto_perception_msgs::msg::ObjectClassification::TRUCK;
} else if (w * l > 2.5 * 7.9) {
classification.label = autoware_auto_perception_msgs::msg::ObjectClassification::BUS;
}
obj.kinematics.orientation_availability =
autoware_auto_perception_msgs::msg::DetectedObjectKinematics::SIGN_UNKNOWN;
break;
case 1:
semantic.type = autoware_perception_msgs::msg::Semantic::PEDESTRIAN;
classification.label = autoware_auto_perception_msgs::msg::ObjectClassification::PEDESTRIAN;
obj.kinematics.orientation_availability =
autoware_auto_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE;
break;
case 2:
semantic.type = autoware_perception_msgs::msg::Semantic::BICYCLE;
classification.label = autoware_auto_perception_msgs::msg::ObjectClassification::BICYCLE;
obj.kinematics.orientation_availability =
autoware_auto_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE;
break;
default:
semantic.type = autoware_perception_msgs::msg::Semantic::UNKNOWN;
}
semantic.confidence = score;
feature_obj.object.semantic = semantic;

geometry_msgs::msg::Pose pose;
pose.position = autoware_utils::createPoint(x, y, z);
pose.orientation = autoware_utils::createQuaternionFromYaw(yaw);
feature_obj.object.state.pose_covariance.pose = pose;

autoware_perception_msgs::msg::Shape shape;
shape.type = autoware_perception_msgs::msg::Shape::BOUNDING_BOX;
shape.dimensions.x = l;
shape.dimensions.y = w;
shape.dimensions.z = h;
feature_obj.object.shape = shape;

// Note: object size is referred from multi_object_tracker
if ((w * l > 2.2 * 5.5) && (w * l <= 2.5 * 7.9)) {
feature_obj.object.semantic.type = semantic.type =
autoware_perception_msgs::msg::Semantic::TRUCK;
} else if (w * l > 2.5 * 7.9) {
feature_obj.object.semantic.type = semantic.type =
autoware_perception_msgs::msg::Semantic::BUS;
classification.label = autoware_auto_perception_msgs::msg::ObjectClassification::UNKNOWN;
obj.kinematics.orientation_availability =
autoware_auto_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE;
}
classification.probability = score;
obj.classification.emplace_back(classification);

obj.kinematics.pose_with_covariance.pose.position = autoware_utils::createPoint(x, y, z);
obj.kinematics.pose_with_covariance.pose.orientation =
autoware_utils::createQuaternionFromYaw(yaw);
obj.shape.type = autoware_auto_perception_msgs::msg::Shape::BOUNDING_BOX;
obj.shape.dimensions = autoware_utils::createTranslation(l, w, h);

output_msg.feature_objects.emplace_back(feature_obj);
output_msg.objects.emplace_back(obj);
}

if (objects_sub_count > 0) {
Expand Down