Notices

• Compilation error under Ubuntu 16.04 LTS Xenial: Issue #4
• Branch for Ubuntu 20.04: devel_2004

L-CAS 3D Point Cloud Annotation Tool 2

• Maintainer status: maintained
• Author: Zhi Yan
• License: GPL-3.0
• Dataset: https://lcas.lincoln.ac.uk/wp/research/data-sets-software/l-cas-3d-point-cloud-people-dataset/

The tool provides a semi-automatic labeling function, means the 3D point cloud data (loaded from the PCD file) is first clustered to provide candidates for labeling, each candidate being a point cluster. Then, the user annotating the data, can label each object by indicating candidate's ID, category, and visibility. A flowchart of this process is shown below.

The quickest way to activate the optional steps is to modify the source code and recompile. 😱

This is a fork of https://github.com/yzrobot/cloud_annotation_tool with some minor modifications.

New features （compared to the first version）

• Option for "Adaptive Clustering" (Optimized for Velodyne VLP-16, please feel free to modify the code for other models.)
• Feature extraction and visualization
• SVM classifier training and prediction

Compiling (tested on Ubuntu 16.04/18.04, failed on 20.04)

Prerequisites

• Qt 4.x: sudo apt-get install libqt4-dev qt4-qmake
• VTK 5.x: sudo apt-get install libvtk5-dev
• PCL 1.7: sudo apt-get install libpcl-1.7-all-dev
• LIBSVM: sudo apt-get install libsvm-dev libsvm-tools

Build and run

• mkdir build
• cd build
• cmake ..
• make
• ./cloud_annotation_tool

Test examples

lcas_simple_data.zip contains 172 consecutive frames (in .pcd file) with 2 fully annotated pedestrians.

Before complaining

• You may need to add negative examples ("Extract background samples" button)
• Make sure you have enough examples for training
• ...

Citation

If you are considering using this tool and the data provided, please reference the following:

@article{yz19auro,
   author = {Zhi Yan and Tom Duckett and Nicola Bellotto},
   title = {Online learning for 3D LiDAR-based human detection: Experimental analysis of point cloud clustering and classification methods},
   journal = {Autonomous Robots},
   year = {2019}
}