Repository Summary
Checkout URI | https://github.com/MOLAorg/mp2p_icp.git |
VCS Type | git |
VCS Version | master |
Last Updated | 2023-11-30 |
Dev Status | DEVELOPED |
CI status | No Continuous Integration |
Released | RELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
mp2p_icp | 0.2.2 |
README
Distro | Build dev | Build releases | Stable version |
---|---|---|---|
ROS 1 Noetic (u20.04) | (ROS 1 obsolete) | ||
ROS 2 Humble (u22.04) | (See root MOLA) | ||
ROS 2 Iron (u22.04) | (See root MOLA) | ||
ROS 2 Rolling (u22.04) | (See root MOLA) |
mp2p_icp
A repertory of multi primitive-to-primitive (MP2P) ICP algorithms in C++.
License: New BSD 3-Clause
Docs: - Main library documentation - C++ API reference
The OLAE-ICP method is described in this technical report:
Jose-Luis Blanco-Claraco. "OLAE-ICP: Robust and fast alignment of geometric
features with the optimal linear attitude estimator", Arxiv 2019.
Introduction
The project provides these C++ libraries:
* mp2p_icp_map
: Provides the mp2p_icp::metric_map_t
generic metric map container.
* mp2p_icp
: With ICP algorithms. It depends on mp2p_icp_map
.
* mp2p_icp_filters
: With point cloud filtering and manipulation algorithms. It depends on mp2p_icp_map
.
This project provides:
* mp2p_icp::metric_map_t
: A generic
data type to store raw or processed point clouds, e.g. segmented, discrete
extracted features. Note that filtering point clouds is intentionally left
outside of the scope of this library.
See MOLA for possible implementations.
* mp2p_icp::ICP_Base
: A uniform API
for matching those generic point clouds.
* Implementations/wrappers of different ICP algorithms under such uniform API.
* The library exposes both, complete iterative ICP algorithms, and the
underlying optimal transformation estimators which are run at each ICP iteration.
mp2p_icp
is used in the MOLA lidar-inertial odometry system.
Implemented Optimal Transformation methods
-
optimal_tf_olae()
: A novel algorithm that can recover the optimal attitude from a set of point-to-point, line-to-line, and plane-to-plane pairings. -
optimal_tf_horn()
: Classic Horn's closed-form optimal quaternion solution. Relies on the implementation in<mrpt/tfest/se3.h>
. -
optimal_gauss_newton()
: Simple non-linear optimizer to find the SE(3) optimal transformation for these pairings: point-to-point, point-to-plane.
Implemented ICP methods
-
ICP_OLAE
: ICP for point clouds, planes, and lines. Usesoptimal_tf_olae()
. -
ICP_Horn_MultiCloud
: Align point clouds layers, using classic Horn's closed-form optimal quaternion solution.
Building
Requisites
- A C++17 compiler. Tested with gcc-7, MSVC 2017.
- Eigen3
- CMake >= 3.4
- MRPT >=2.11.3 (If you need to use an older version of MRPT, use mp2p_icp<=0.2.2)
Install all the dependencies in Ubuntu with:
# MRPT >=2.4.0, for now from this PPA (or build from sources if preferred):
sudo add-apt-repository ppa:joseluisblancoc/mrpt
sudo apt update
sudo apt install libmrpt-dev
# Rest of dependencies:
sudo apt install build-essential cmake libeigen3-dev
Build
cmake -H. -Bbuild
cd build
cmake --build . # or make
Run the tests
make test
Run the demos
# 2D icp with point-to-point pairings only:
build/bin/mp2p-icp-run \
--input-local demos/local_001.mm \
--input-global demos/global_001.mm \
-c demos/icp-settings-2d-lidar-example-point2point.yaml \
--generate-debug-log
# Inspect the debug log:
build/bin/mp2p-icp-log-viewer
# 2D icp with point-to-line pairings:
build/bin/mp2p-icp-run \
--input-local demos/local_001.mm \
--input-global demos/global_001.mm \
-c demos/icp-settings-2d-lidar-example-point2line.yaml \
--generate-debug-log
# Inspect the debug log:
build/bin/mp2p-icp-log-viewer
# 3D icp with external library wrapper
build/bin/mp2p-icp-run \
--input-local demos/local_001.mm \
--input-global demos/global_001.mm \
-c demos/icp-settings-example-libpointmatcher.yaml \
--generate-debug-log
# Inspect the debug log:
build/bin/mp2p-icp-log-viewer