A comprehensive C++ toolkit for mobile robotics and computer vision research, providing libraries for SLAM, Bayesian filtering, and 3D geometry.
MRPT (Mobile Robot Programming Toolkit) is an open-source C++ library collection for mobile robotics and computer vision research. It provides implementations of essential algorithms like SLAM, Bayesian filtering, spatial transformations, and image processing, enabling developers to build and test robotic systems efficiently. The toolkit addresses the need for reliable, reusable components in academic and industrial robotics projects.
Researchers, PhD students, and engineers working on mobile robotics, autonomous systems, or computer vision who need robust, peer-reviewed algorithms for SLAM, state estimation, and perception.
MRPT offers a comprehensive, modular, and performance-optimized codebase with extensive documentation and examples, reducing development time for complex robotic applications. Its integration with ROS and support for multiple platforms make it a versatile choice for both prototyping and deployment.
:zap: The Mobile Robot Programming Toolkit (MRPT)
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Provides implementations of multiple SLAM solutions for 2D and 3D environments, with documented examples and integration into frameworks like MOLA, making it a go-to for robotics research.
Emphasizes modularity with well-tested C++ components for spatial geometry, Bayesian inference, and image processing, supported by extensive API documentation and example code.
Offers ROS packages for both ROS 1 and ROS 2, facilitating seamless use in robotic operating systems, with clear installation instructions and wrappers.
Includes ready-to-use desktop apps for camera calibration, dataset inspection, and visualization, which streamline common robotics workflows without extra development.
Building from sources requires managing dependencies and following detailed compilation guides, which can be daunting and error-prone for newcomers or teams on tight deadlines.
While Python bindings exist, they are secondary to C++, making it less ideal for projects heavily reliant on Python for machine learning or rapid iteration.
Supported distributions are limited to specific Ubuntu versions, and Windows users must rely on nightly builds, which may introduce instability or compatibility issues.