Package Summary

darknet_ros_3d

• Maintainer status: maintained
• Maintainer: Fernando González Ramos [email protected]
• Author: Francisco Martín Rico [email protected]; Fernando González Ramos [email protected]
• License: BSD
• Source: Github

Contents

• Overview
  • Previous steps
• Quick start
  • Instalation
  • How it works
• Nodes
  • darknet3d_node

Overview

darknet_ros_3d provides you 3d bounding boxes of the objects contained in an objects list, where is specificated the 3d position of each object. Using a RGBD camera like Asus Xtion, Orbbec Astra or Intel Realsense and neuronal network darknet ros, objects can be detected and his position can be calculated.

In addition, there is a visual debugger tool based on visual markers that you can see with rviz

Previous Steps

The first we have to know is that darknet_ros_3d package have dependencies as follow:

• rclcpp
• rclcpp_lifecycle
• darknet_ros_msgs
• gb_visual_detection_3d_msgs
• sensor_msgs
• tf2
• tf2_ros
• tf2_sensor_msgs
• visualization_msgs
• eigen3_cmake_module
• Eigen3

You can install Darknet Ros following this steps. NOTE: ros2 branch

Quick Start

Instalation

You must to clone gb_visual_detection_3d into src folder located in your workspace. Later, you have to compile it typing colcon build.

How It Works

First of all, is necessary to run camera driver. Make sure that camera driver is publishing point cloud information.

Later, you must run darknet_ros and, if everything worked properly, you should see 2d bounding boxes in your screen. If not, you have a problem with darknet_ros package.

Now, you can run darknet_ros_3d typing ros2 launch darknet_ros_3d darknet_ros_3d.launch.py. If you want to change default parameters like topics it subscribe, you can change it in the configuration file located at ~/catkin_ws/src/gb_visual_detection_3d/darknet_ros_3d/darknet_ros_3d/config/. Default parameters are the following:

• interested_classes: Classes you want to detect. It must be classes names than exists previously in darknet ros.

• mininum_detection_threshold: Maximum distance range between any pixel of image and the center pixel of the image to be considered.

• minimum_probability: Minimum object probability (provided by darknet_ros) to be considered.

• darknet_ros_topic: topic where darknet_ros publicates it's bounding boxes. /darknet_ros/bounding_boxes.

• point_cloud_topic: topic where point cloud is published from camera. By default: /camera/pointcloud. It is important that point cloud topic be of PointCloud2 type and it be depth_registered.

• working_frame: frame that all measurements are based on. By default, camera_link. It is very important that if you want to change this frame, it has the same axes than camera_link, if you would want 3d coordinates in another axis, you must change it later (once 3d bounding box has been calculated).

Nodes

darknet3d_node

darknet3d_node provide bounding boxes. This bounding boxes are combinated with point cloud information to calculate (xmin, ymin, zmin) and (xmax, ymax, zmax) 3D coordinates.

Then, darknet_ros_3d publicates it's own bounding boxes array of BoundingBoxes3d type in /darknet_ros_3d/bounding_boxes_3d by default, which is an array of BoundingBox3d that contains the following information:

std_msgs/Header header
BoundingBox3d[] bounding_boxes

BoundingBox3d:

string object_name
float64 probability
float64 xmin
float64 ymin
float64 xmax
float64 ymax
float64 zmin
float64 zmax

• object_name: Object name.
• probability: Probability of certainty.
• xmin: X coordinate in meters of left upper corner of bounding box.
• xmax: X coordinate in meters of right lower corner of bounding box.
• ymin: Y coordinate in meters of left upper corner of bounding box.
• ymax: Y coordinate in meters of right lower corner of bounding box.
• zmin: Z coordinate in meters of nearest pixel of bounding box.
• zmax: Z coordinate in meters of the furthest pixel of bounding box.

An example of output is as follow:

header:
  stamp:
    sec: 1593723845
    nanosec: 430724839
  frame_id: camera_link
bounding_boxes:
- object_name: person
  probability: 0.7609682679176331
  xmin: 0.4506256580352783
  ymin: -0.3164764642715454
  xmax: 0.7936256527900696
  ymax: 0.11368180811405182
  zmin: -0.25958430767059326
  zmax: 0.10506562888622284

You can visualize the markers on rviz adding MarkerArray and subscribing to topic /darknet_ros_3d/markers.