Automated TLS Multi-Scan Registration in Forest Environments: An end-to-end solution based on Hash Table
Terrestrial laser scanning (TLS) has proven to be an effective tool for forest inventories due to its accurate, non-destructive capability to document 3D space structures. The multi-scan mode of TLS enables comprehensive data acquisition, but the point cloud of each scan must be aligned into a unified coordinate. In practice, the most common solution involves manually placing artificial markers in the field, which is time-consuming and labor-intensive. Consequently, the automated multi-scan registration method is highly appreciated for subsequent applications. This study presents an automated TLS multi-scan registration algorithm for forest point clouds, HashReg, utilizing the high-efficiency operations of Hash Table.
The point clouds from multiple scans are represented in different colors and transformed into common coordinate, with the scan positions are represented by a series of black circles.
To better visualize the internal details of each plot, the area marked with the red dashed box is extracted for a cross-section view, displayed in the top-right part of the subfigure. Further, some trees are randomly selected and zoomed in to reveal the registration result. The close-up views of the stem, branch and even structure of the canopy are provided in the bottom-right part of each subfigure.
👉👉👉 : If you are interested to tested them with HashReg, here is a manual for quick start.
The code have been tested on Ubuntu 18.04. The third-party library are listed as follows:
PCL >=1.10 (use 1.12 in this project)
libLAS (1.8.1)
Eigen >=3.3.4
OpenCV >=3.3.1
CSF (Cloth Simulation Filter)
small_gicp
GTSAM
OpenMP
To install a newer version in Ubuntu 18.04, you can download and compile PCL through the source code: https://github.com/PointCloudLibrary/pcl.
Note: Install PCL 1.12 in different path with PCL 1.8(System), to avoid version confiliction.
Visit Download or GitHub Repository to get the source code of libLAS.
cd liblas or your souce-code-path
mkdir makefiles
cd makefilesConfigure the basic core library for the “Unix Makefiles” target:
cmake -G "Unix Makefiles" ../
-- The C compiler identification is GNU
-- The CXX compiler identification is GNU
-- Checking whether C compiler has -isysroot
-- Checking whether C compiler has -isysroot - yes
-- Check for working C compiler: /usr/bin/gcc
-- Check for working C compiler: /usr/bin/gcc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Checking whether CXX compiler has -isysroot
-- Checking whether CXX compiler has -isysroot - yes
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Enable libLAS utilities to build - done
-- Configuring done
-- Generating done
-- Build files have been written to: /Users/hobu/hg/liblas-cmake/makefilesCompile this code and install.
make
sudo make installFinally, test by run lasinfo.
lasinfo Path_To_Your_Data/XXX.lasIn your terminal, you should see the output like this.
---------------------------------------------------------
Header Summary
---------------------------------------------------------
Version: 1.2
Source ID: 0
Reserved: 0
Project ID/GUID: '00000000-0000-0000-0000-000000000000'
System ID: 'libLAS'
Generating Software: 'libLAS 1.8.1'
File Creation Day/Year: 230/2024
Header Byte Size 227
Data Offset: 227
Header Padding: 0
Number Var. Length Records: None
Point Data Format: 3
Number of Point Records: 21433359
Compressed: False
Number of Points by Return: 0 0 0 0 0 You can install the eigen library through apt way.
sudo apt update
sudo apt-get install libeigen3-devOr you can just download and extract Eigen's source code: https://eigen.tuxfamily.org/index.php?title=Main_Page
You can install the OpenCV library through apt way.
sudo apt update
sudo apt install libopencv-dev python3-opencv
# check opencv version
pkg-config --modversion opencvCSF (Cloth Simulation Filter) is an algorithm for LiDAR ground filtering. It usually employed to the Airborne Lidar data, but also shown good performance on the Terrastrial-, Mobile-, and even Spaceborne- Lidar.
You can follow the instruction of CSF.
mkdir build #or other name
cd build
cmake ..
make
sudo make installRelated papers: W. Zhang, J. Qi*, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan, “An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation,” Remote Sens., vol. 8, no. 6, p. 501, 2016.
small_gicp is a header-only C++ library providing efficient and parallelized algorithms for fine point cloud registration (ICP, Point-to-Plane ICP, GICP, VGICP, etc.). The C++ version of small_gicp can be installed by following commands.
sudo apt-get install libeigen3-dev libomp-dev
cd small_gicp
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release && make -j
sudo make installIn this program, small_gicp is employed to re-fine registration.
Clone this repository, then compile.
git clone https://github.com/xchwang1998/Forest_TLS_Reg.git
cd ./Forest_TLS_Reg
mkdir build && cd build
cmake ..
makeThere are several node for different missions in this project, such as:
- Registrates two TLS stations
./Reg2TLSPoints [Target Station's File Name] [Source Station's File Name]Note: You should first mkdir your_data_folder and revise the corresponding path in source code.
- Registrates multiple TLS stations
Place your data in corresponding path, then run MultiRegTLSPoints.
./MultiRegTLSPoints- Registrates multiple TLS stations in FGI forest datasets, Tongji Trees, and ETH trees.
# run MultiRegFGI, and input the number of TLS stations
./MultiRegFGI [NUM of Stations]
# run MultiRegTongji, and input Plot-ID
./MultiRegTongji [Plot NUM, 1-4]
# run MultiRegETH, and input the number of TLS stations
./MultiRegETH [NUM of Stations]If you use this repository, please cite our paper.
If you have any questions, please do not hesitate to contact me: xchwang@whu.edu.cn