pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM
This provides a modular and extensible framework for researchers and beginners in Visual SLAM, though it is incremental as it builds on existing methods rather than introducing new paradigms.
The authors tackled the need for a flexible and accessible framework for Visual SLAM by developing pySLAM, an open-source Python tool that supports various camera inputs and integrates classical and learning-based features, enabling rapid prototyping and evaluation across diverse datasets.
pySLAM is an open-source Python framework for Visual SLAM that supports monocular, stereo, and RGB-D camera inputs. It offers a flexible and modular interface, integrating a broad range of both classical and learning-based local features. The framework includes multiple loop closure strategies, a volumetric reconstruction pipeline, and support for depth prediction models. It also offers a comprehensive set of tools for experimenting with and evaluating visual odometry and SLAM modules. Designed for both beginners and experienced researchers, pySLAM emphasizes rapid prototyping, extensibility, and reproducibility across diverse datasets. Its modular architecture facilitates the integration of custom components and encourages research that bridges traditional and deep learning-based approaches. Community contributions are welcome, fostering collaborative development and innovation in the field of Visual SLAM. This document presents the pySLAM framework, outlining its main components, features, and usage.