pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM

TL;DR

pySLAM is an open-source, modular Python framework for Visual SLAM supporting various camera types, integrating classical and learning-based features, and facilitating research, experimentation, and reproducibility.

Contribution

It introduces a flexible, extensible framework that combines traditional and deep learning approaches for Visual SLAM, promoting rapid prototyping and collaborative development.

Findings

Supports monocular, stereo, and RGB-D inputs

Includes multiple loop closure and volumetric reconstruction methods

Facilitates experimentation with classical and learning-based features

Abstract

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.