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ros_front_detection_segmentation

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The goal of the project is to build a ROS node that would be responsible for detecting rotational fronts and transitional fronts and then perform a segmentation of the fronts. The module is based on matterport's Mask RCNN implementation. The data used for training, evaluation and testing 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 works best with the other three modules that can be found here:

The node utilizes conda virtual environment to separate the environment variables such as Tensorflow version or CUDA version.

Results

Detection

  • mAP@IoU=.50 -> 0.77
  • mAP@IoU=.75 -> 0.71
  • mAP@IoU=.90 -> 0.38

Segmentation

  • Dice score of rotational fronts -> 0.82
  • Dice score of transitional fronts -> 0.77

Dependencies

  • Ubuntu 20.04
  • ROS Noetic
  • Anaconda

Installation

  • Create conda environment from environment.yml file conda env create -f environment.yml
  • Activate environment conda activate ros_mask_rcnn
  • 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_mask_rcnn/bin/python3.6
  • Clone the repository
source devel/setup.bash
cd src
git clone https://github.com/arekmula/ros_front_detection_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 mrcnn_model_dir "path/to/model/mask_rcnn_model.h5"
rosparam set front_prediction_topic "topic/to/publish/prediction"
rosparam set visualize_front_prediction True/False
  • Run with
rosrun front_detection_segmentation front_detection_segmentation_node.py