Official page of Patchwork (RA-L'21 w/ IROS'21)

Overview

Patchwork

Official page of "Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor", which is accepted by RA-L with IROS'21 option

[Video] [Preprint Paper] [Project Wiki]

Patchwork Concept of our method (CZM & GLE)

It's an overall updated version of R-GPF of ERASOR [Code] [Paper].


Demo

KITTI 00

Rough Terrain


Characteristics

  • Single hpp file (include/patchwork/patchwork.hpp)

  • Robust ground consistency

As shown in the demo videos and below figure, our method shows the most promising robust performance compared with other state-of-the-art methods, especially, our method focuses on the little perturbation of precision/recall as shown in this figure.

Please kindly note that the concept of traversable area and ground is quite different! Please refer to our paper.

Contents

  1. Test Env.
  2. Requirements
  3. How to Run Patchwork
  4. Citation

Test Env.

The code is tested successfully at

  • Linux 18.04 LTS
  • ROS Melodic

Requirements

ROS Setting

    1. Install ROS on a machine.
    1. Thereafter, jsk-visualization is required to visualize Ground Likelihood Estimation status.
sudo apt-get install ros-melodic-jsk-recognition
sudo apt-get install ros-melodic-jsk-common-msgs
sudo apt-get install ros-melodic-jsk-rviz-plugins
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone https://github.com/LimHyungTae/patchwork.git
cd .. && catkin build patchwork 

How to Run Patchwork

We provide three examples

  • Offline KITTI dataset
  • Online (ROS Callback) KITTI dataset
  • Own dataset using pcd files

Offline KITTI dataset

  1. Download SemanticKITTI Odometry dataset (We also need labels since we also open the evaluation code! :)

  2. Set the data_path in launch/offline_kitti.launch for your machine.

The data_path consists of velodyne folder and labels folder as follows:

data_path (e.g. 00, 01, ..., or 10)
_____velodyne
     |___000000.bin
     |___000001.bin
     |___000002.bin
     |...
_____labels
     |___000000.label
     |___000001.label
     |___000002.label
     |...
_____...
   
  1. Run launch file
roslaunch patchwork offline_kitti.launch

You can directly feel the speed of Patchwork! 😉

Online (ROS Callback) KITTI dataset

We also provide rosbag example. If you run our patchwork via rosbag, please refer to this example.

  1. Download readymade rosbag
wget https://urserver.kaist.ac.kr/publicdata/patchwork/kitti_00_xyzilid.bag
  1. After building this package, run the roslaunch as follows:
roslaunch patchwork rosbag_kitti.launch
  1. Then play the rosbag file in another command
rosbag play kitti_00_xyzilid.bag

Own dataset using pcd files

Please refer to /nodes/offilne_own_data.cpp.

(Note that in your own data format, there may not exist ground truth labels!)

Be sure to set right params. Otherwise, your results may be wrong as follows:

W/ wrong params After setting right params

For better understanding of the parameters of Patchwork, please read our wiki, 4. IMPORTANT: Setting Parameters of Patchwork in Your Own Env..

Offline (Using *.pcd or *.bin file)

  1. Utilize /nodes/offilne_own_data.cpp

  2. Please check the output by following command and corresponding files:

roslaunch patchwork offline_ouster128.launch

Online (via rosbag)

  1. Utilize rosbag_kitti.launch.

  2. To do so, remap the topic of subscriber, e.g. add remap line as follows:

<remap from="/node" to="$YOUR_LIDAR_TOPIC_NAME$"/>
  1. In addition, minor modification of ros_kitti.cpp is necessary by refering to offline_own_data.cpp.

Citation

If you use our code or method in your work, please consider citing the following:

@article{lim2021patchwork,
title={Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor},
author={Lim, Hyungtae and Minho, Oh and Myung, Hyun},
journal={IEEE Robotics and Automation Letters},
year={2021}
}

Description

All explanations of parameters and other experimental results will be uploaded in wiki

Contact

If you have any questions, please let me know:

TODO List

  • Add ROS support
  • Add preprint paper
  • Add demo videos
  • Add own dataset examples
  • Update wiki

Owner
Hyungtae Lim
Ph.D Candidate of URL lab. @ KAIST, South Korea
Hyungtae Lim
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs

Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs ArXiv Abstract Convolutional Neural Networks (CNNs) have become the de f

Philipp Benz 12 Oct 24, 2022
OSLO: Open Source framework for Large-scale transformer Optimization

O S L O Open Source framework for Large-scale transformer Optimization What's New: December 21, 2021 Released OSLO 1.0. What is OSLO about? OSLO is a

TUNiB 280 Nov 24, 2022
TensorFlow implementation of ENet, trained on the Cityscapes dataset.

segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e

Fredrik Gustafsson 248 Dec 16, 2022
Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation

tf-imle Tensorflow 2 and PyTorch implementation and Jupyter notebooks for Implicit Maximum Likelihood Estimation (I-MLE) proposed in the NeurIPS 2021

NEC Laboratories Europe 69 Dec 13, 2022
USAD - UnSupervised Anomaly Detection on multivariate time series

USAD - UnSupervised Anomaly Detection on multivariate time series Scripts and utility programs for implementing the USAD architecture. Implementation

116 Jan 04, 2023
Train Yolov4 using NBX-Jobs

yolov4-trainer-nbox Train Yolov4 using NBX-Jobs. Use the powerfull functionality available in nbox-SDK repo to train a tiny-Yolo v4 model on Pascal VO

Yash Bonde 1 Jan 12, 2022
Self-training for Few-shot Transfer Across Extreme Task Differences

Self-training for Few-shot Transfer Across Extreme Task Differences (STARTUP) Introduction This repo contains the official implementation of the follo

Cheng Perng Phoo 33 Oct 31, 2022
A Player for Kanye West's Stem Player. Sort of an emulator.

Stem Player Player Stem Player Player Usage Download the latest release here Optional: install ffmpeg, instructions here NOTE: DOES NOT ENABLE DOWNLOA

119 Dec 28, 2022
A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations

jaxdf - JAX-based Discretization Framework Overview | Example | Installation | Documentation ⚠️ This library is still in development. Breaking changes

UCL Biomedical Ultrasound Group 65 Dec 23, 2022
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

DensePose: Dense Human Pose Estimation In The Wild Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos [densepose.org] [arXiv] [BibTeX] Dense human pos

Meta Research 6.4k Jan 01, 2023
Conceptual 12M is a dataset containing (image-URL, caption) pairs collected for vision-and-language pre-training.

Conceptual 12M We introduce the Conceptual 12M (CC12M), a dataset with ~12 million image-text pairs meant to be used for vision-and-language pre-train

Google Research Datasets 226 Dec 07, 2022
Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

Aesara 898 Jan 07, 2023
Alfred-Restore-Iterm-Arrangement - An Alfred workflow to restore iTerm2 window Arrangements

Alfred-Restore-Iterm-Arrangement This alfred workflow will list avaliable iTerm2

7 May 10, 2022
This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"

Differentiable Volumetric Rendering Paper | Supplementary | Spotlight Video | Blog Entry | Presentation | Interactive Slides | Project Page This repos

697 Jan 06, 2023
SPT_LSA_ViT - Implementation for Visual Transformer for Small-size Datasets

Vision Transformer for Small-Size Datasets Seung Hoon Lee and Seunghyun Lee and Byung Cheol Song | Paper Inha University Abstract Recently, the Vision

Lee SeungHoon 87 Jan 01, 2023
YOLOX-CondInst - Implement CondInst which is a instances segmentation method on YOLOX

YOLOX CondInst -- YOLOX 实例分割 前言 本项目是自己学习实例分割时,复现的代码. 通过自己编程,让自己对实例分割有更进一步的了解。 若想

DDGRCF 16 Nov 18, 2022
[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

BCMI 49 Jul 27, 2022
Neural Message Passing for Computer Vision

Neural Message Passing for Quantum Chemistry Implementation of different models of Neural Networks on graphs as explained in the article proposed by G

Pau Riba 310 Nov 07, 2022
Analysis of rationale selection in neural rationale models

Neural Rationale Interpretability Analysis We analyze the neural rationale models proposed by Lei et al. (2016) and Bastings et al. (2019), as impleme

Yiming Zheng 3 Aug 31, 2022
A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

Evan 1.3k Jan 02, 2023