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mp2p_icp

C++ libraries for multi primitive-to-primitive (MP2P) ICP algorithms and flexible point cloud processing pipelines. mp2p_icp is used in the mola_lidar_odometry framework:

• Main library documentation and C++ API
• License: New BSD 3-Clause (Note that each module of MOLA has its own license)

Introduction

The project provides these C++ libraries:

• mp2p_icp_map: Provides the mp2p_icp::metric_map_t generic metric map container. Metric map files with extension *.mm are serializations of instances of this class.
• 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.

And these applications:

• kitti2mm: Converts KITTI-like .bin files to .mm files.
• mm-filter: CLI tool to apply a pipeline to an input metric map (*.mm), saving the result as another metric map file.
• mm-info: CLI tool to read a metric map (*.mm) and describe its contents.
• mm-viewer: GUI tool to visualize .mm (metric map) files.
• mm2txt: CLI tool to export the layers of a metric map (*.mm) as CSV/TXT.
• icp-log-viewer: GUI to inspect results from ICP runs.
• icp-run: Standalone program to run ICP pipelines.
• sm2mm: A CLI tool to convert a simple map *.simplemap (from a SLAM mapping session) into a metric map (*.mm) via a configurable pipeline configuration file.
• sm-cli: A CLI tool to inspect, visualize, or modify simple maps *.simplemap (from a SLAM mapping session).
• txt2mm: CLI tool to convert pointclouds from CSV/TXT files to mp2p_icp mm.

Key C++ classes provided by this project (see full docs):

• 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.