/Tiny-Obstacle-Discovery-ROS

Official Python/ROS Implementation for "A Novel Multi-layer Framework for Tiny Obstacle Discovery", ICRA 2019

Primary LanguageC++

Tiny-Obstacle-Discovery-ROS

Created by Yicong Chang, Yankai Wang, Fei Sheng, and Feng Xue from BUPT

Official Python/ROS Implementation for "A Novel Multi-layer Framework for Tiny Obstacle Discovery", ICRA 2019

Introduction

This repository contains the official Python/ROS implementation for "A Novel Multi-layer Framework for Tiny Obstacle Discovery". This paper has been accepted by IEEE International Conference on Robotics and Automation (ICRA) 2019.

Note: This version has been slightly different from the Matlab Version, mainly including the following points:

  • The feature, namely, mode of edge pixels, is slightly simplified to accelerate the algorithm.
  • For higher efficiency, many modules in this system are implemented in C++.
  • The modules in the system are largely accelerated, although there is still a lot of room for improvement.
  • The performance of this implementation is slightly lower than that of the Matlab implementation

Citation

If you find the paper or the code useful, please cite our paper:

@INPROCEEDINGS{Xue_ICRA_2019,
Author = {Feng Xue, Anlong Ming, Menghan Zhou and Yu Zhou},
booktitle={2019 International Conference on Robotics and Automation (ICRA)},
title={A Novel Multi-layer Framework for Tiny Obstacle Discovery},
year={2019},
}

Visualization of generated bounding boxes

graph

Data Preparation

Lost and Found Dataset (LAF) [4]

You may download the original images from here and annotations from here. Then, you will have the following directory structure:

lostandfound
|_ leftImg8bit
|  |_ test
|  |  |_ 02_Hanns_Klemm_Str_44
|  |  |_ ...
|  |  |_ 15_Rechbergstr_Deckenpfronn
|  |_ train
|  |  |_ 01_Hanns_Klemm_Str_45
|  |  |_ ...
|  |  |_ 14_Otto_Lilienthal_Str_24
|_ gtCoarse 
|  |_ test
|  |  |_ 02_Hanns_Klemm_Str_44
|  |  |_ ...
|  |  |_ 15_Rechbergstr_Deckenpfronn
|  |_ train
|  |  |_ 01_Hanns_Klemm_Str_45
|  |  |_ ...
|  |  |_ 14_Otto_Lilienthal_Str_24

Before Training and Testing

  1. The packages of our environment is listed as follows:

    • Ubuntu 16.04
    • ROS Kinetic
    • Python 3.5.2
    • Numpy 1.18.2
    • Boost_Python 1.58.0
    • OpenCV 3.3.0
    • Matplotlib 3.0.3
    • scikit-learn 0.22.2
    • scipy 1.4.1
    • joblib 0.14.1

  2. To avoid the wrong import of OpenCV2 given by ROS, remove cv2.so in the ROS's python path : /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so

  3. Compile the C++ code

cd ./pyboostcvconverter
mkdir build
cd build
cmake ..
make -j8

  1. Copy the file pbcvt.* from ./pyboostcvconverter/build/ to ./TOD_ros/src/tod/scripts/tools/

  2. Compile the ROS workspace

cd TOD_ros
catkin_make
  1. If you found some errors in compiling the workspace, please delete "devel", " build", and try again.

Testing

For testing the algorithm on lost and found dataset, you can run the launch file:

cd TOD_ros
source ./devel/setup.bash
roslaunch tod test.launch

The graph of ROS can be seen as follows: graph

Note for test.launch:

  • exp_name is the name of experiments, it determines the name of final performance file.
  • exp_path is the path of all experiments files, please set it.

Note for get_params_LAF.py:

  • abspath_LAF is the path of Lost and Found dataset.

Evaluation

The evaluations will be conducted when all the images are processed. Eventually, two files recorded the performances are created:

./TOD_ros/src/tod/scripts/result/recall/recall_(exp_name).mat
./TOD_ros/src/tod/scripts/result/ROC/ROC_(exp_name).mat

Option

For visualization, you needs to add performance files into the ros_names list and the recall_names list in draw_result.py, and run the script:

cd ./TOD_ros/src/tod/scripts/
python3 draw_result.py

Performance

Training

If you want to train your own model, run the following code.

cd ./TOD_ros/src/tod/scripts/
python3 train_ICRA.py

Reference

[1] Dollar P , Zitnick C L . Fast Edge Detection Using Structured Forests[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(8):1558-1570.

[2] Zitnick C L , Dollar P . Edge Boxes: Locating Object Proposals from Edges[C]// European Conference on Computer Vision. Springer, Cham, 2014.

[3] Ma J , Ming A , Huang Z , et al. Object-Level Proposals[C]// IEEE International Conference on Computer Vision. IEEE, 2017.

[4] Pinggera P , Ramos S , Gehrig S , et al. Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016.