开源视觉SLAM框架，支持多相机输入与深度学习融合。
pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM
- 模块化设计，兼容传统与深度学习特征
- 支持单目、双目、RGB-D输入及回环检测
- 适合初学者与研究者快速验证算法
pySLAM 是一个开源的 Python 视觉 SLAM 框架，支持单目、双目和 RGB-D 相机输入。它提供灵活的模块化接口，集成多种经典与基于学习的局部特征。框架包含多种回环检测策略、体素重建流水线以及深度预测模型支持，并提供全面的工具用于视觉里程计与 SLAM 模块的实验与评估。适用于初学者与经验丰富的研究人员，强调快速原型设计、可扩展性与跨数据集复现性。其模块化架构便于自定义组件集成，促进传统方法与深度学习方法的融合研究。欢迎社区贡献，推动视觉 SLAM 领域的协作开发与创新。本文介绍 pySLAM 框架的主要组件、功能与使用方式。
原文摘要 · Abstract (English)
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.
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