ros_joint_detection

               

The goal of the project is to build a ROS node that would be responsible for detecting rotational joints. The module uses a neural network to perform the task and utilizes U-Net architecture with EfficientNetB0 working as a backend. The dataset used for training, evaluation, and testing the neural network is available here.

This module is part of my master thesis "Point cloud-based model of the scene enhanced with information about articulated objects" and requires two other modules to work properly:

• Handler detector
• Front detector

The third module Articulated objects scene builder utilize results of the rest of the modules to find articulated objects in a 3D environment.

How it works

               

The module utilizes the results of the handler detection module as well as the front detection module to prepare input for the neural network. The input of the network has 5 channels:

• RGB image
• Handlers mask
• Rotational fronts mask

The prediction results are then post-processed to get rid of false positive predictions and to attach a rotational joint to a rotational object.

Results

• Accuracy: 61.02 %
• False positive rate: 3.54 %

Dependencies

• ROS Noetic
• Anaconda
• image-classifiers pip install image-classifiers
• efficientnet pip install efficientnet

Installation

• Create conda environment from environment.yml file conda env create -f environment.yml
• Activate environment conda activate ros_joint_seg
• Create catkin workspace with Python executable set from conda:

source /opt/ros/noetic/setup.bash
mkdir -p caktin_ws/src
cd catkin_ws
catkin_make -DPYTHON_EXECUTABLE=~/anaconda3/envs/ros_joint_seg/bin/python3.8

• Clone the repository

source devel/setup.bash
cd src
git clone https://github.com/arekmula/ros_joint_segmentation
cd ~/catkin_ws
catkin_make

Run with

From activated conda environment run following commands (remember to source ROS base and devel environment):

• Setup ROS parameters:

rosparam set rgb_image_topic "image/topic"
rosparam set joint_prediction_topic "topic/to/publish/prediction"
rosparam set visualize_joint_prediction True/False
rosparam set joint_seg_model "path/to/model/model.h5"

• Run with

rosrun ros_joint_segmentation joint_segmentation.py