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

• URL: http://arxiv.org/abs/2502.11955v2
• Date: Wed, 19 Feb 2025 12:27:07 GMT
• Title: pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM
• Abstract summary: pySLAM is an open-source Python framework for Visual SLAM.<n>It supports monocular, stereo, and RGB-D cameras.<n> pySLAM encourages community contributions, fostering collaborative development in the field of Visual SLAM.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: pySLAM is an open-source Python framework for Visual SLAM, supporting monocular, stereo, and RGB-D cameras. It provides a flexible interface for integrating both classical and modern local features, making it adaptable to various SLAM tasks. The framework includes different loop closure methods, a volumetric reconstruction pipeline, and support for depth prediction models. Additionally, it offers a suite of tools for visual odometry and SLAM applications. Designed for both beginners and experienced researchers, pySLAM encourages community contributions, fostering collaborative development in the field of Visual SLAM.

Related papers

• SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing [77.87631792556942]
  SLAM-LLM is an open-source framework designed to train customized Multimodal Large Language Models (MLLMs)<n>It provides a modular configuration of different encoders, projectors, LLMs, and parameter-efficient fine-tuning plugins.<n>It includes high-performance checkpoints like Automatic Speech Recognition (ASR), Automated Audio Captioning (AAC), and Music Captioning (MC)
  arXiv  Detail & Related papers  (2026-01-14T11:25:36Z)
• KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging [1.2556993688873865]
  KonfAI is a modular and fully deep learning framework specifically designed for medical imaging tasks.<n>It enables users to define complete training, inference, and evaluation through structured YAML configuration files.<n>KonfAI is open source and available at https://github.com/vboussot/KonfAI.
  arXiv  Detail & Related papers  (2025-08-13T13:55:43Z)
• Product of Experts for Visual Generation [60.91134809173301]
  We propose a Product of Experts (PoE) framework that performs inference-time knowledge composition from heterogeneous models.<n>Our framework shows practical benefits in image and video synthesis tasks, yielding better controllability than monolithic methods.
  arXiv  Detail & Related papers  (2025-06-10T15:21:14Z)
• VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets [64.57742015099531]
  VSLAM-LAB is a unified framework designed to streamline the development, evaluation, and deployment of VSLAM systems. It enables seamless compilation and configuration of VSLAM algorithms, automated dataset downloading and preprocessing, and standardized experiment design, execution, and evaluation.
  arXiv  Detail & Related papers  (2025-04-06T12:02:19Z)
• DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection [48.257506587226324]
  We introduce DitHub, a framework designed to build and maintain a library of efficient adaptation modules.<n>Inspired by Version Control Systems, DitHub manages expert modules as branches that can be fetched and merged as needed.<n>Our method achieves state-of-the-art performance on the ODinW-13 benchmark and ODinW-O, a newly introduced benchmark designed to assess class reappearance.
  arXiv  Detail & Related papers  (2025-03-12T11:15:34Z)
• Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines [5.277598111323804]
  This paper surveys the current state of visual SLAM algorithms according to the two main frameworks: geometry-based and learning-based SLAM.<n>We address two significant issues in surveying visual SLAM, providing a consistent classification of visual SLAM pipelines and (2) a robust evaluation of their performance under different deployment conditions.
  arXiv  Detail & Related papers  (2025-03-04T19:20:17Z)
• Self-Organizing Edge Computing Distribution Framework for Visual SLAM [0.6749750044497732]
  We propose a novel edge-assisted SLAM framework capable of self-organizing fully distributed SLAM execution across a network of devices.<n>The architecture consists of three layers and is designed to be device-agnostic, resilient to network failures, and minimally invasive to the core SLAM system.
  arXiv  Detail & Related papers  (2025-01-15T07:24:15Z)
• Large Action Models: From Inception to Implementation [51.81485642442344]
  Large Action Models (LAMs) are designed for action generation and execution within dynamic environments.<n>LAMs hold the potential to transform AI from passive language understanding to active task completion.<n>We present a comprehensive framework for developing LAMs, offering a systematic approach to their creation, from inception to deployment.
  arXiv  Detail & Related papers  (2024-12-13T11:19:56Z)
• XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM [5.092026311165656]
  XRDSLAM is a flexible SLAM framework that adopts a modular code design and a multi-process running mechanism. Within this framework, we integrate several state-of-the-art SLAM algorithms with different types, including NeRF and 3DGS based SLAM, and even odometry or reconstruction algorithms. We contribute all the code, configuration and data to the open-source community, which aims to promote the widespread research and development of SLAM technology.
  arXiv  Detail & Related papers  (2024-10-31T07:25:39Z)
• OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models [61.14336781917986]
  We introduce OpenR, an open-source framework for enhancing the reasoning capabilities of large language models (LLMs) OpenR unifies data acquisition, reinforcement learning training, and non-autoregressive decoding into a cohesive software platform. Our work is the first to provide an open-source framework that explores the core techniques of OpenAI's o1 model with reinforcement learning.
  arXiv  Detail & Related papers  (2024-10-12T23:42:16Z)
• MALPOLON: A Framework for Deep Species Distribution Modeling [3.1457219084519004]
  MALPOLON aims to facilitate training and inferences of deep species distribution models (deep-SDM) It is written in Python and built upon the PyTorch library. The framework is open-sourced on GitHub and PyPi.
  arXiv  Detail & Related papers  (2024-09-26T17:45:10Z)
• pyvene: A Library for Understanding and Improving PyTorch Models via Interventions [79.72930339711478]
  $textbfpyvene$ is an open-source library that supports customizable interventions on a range of different PyTorch modules. We show how $textbfpyvene$ provides a unified framework for performing interventions on neural models and sharing the intervened upon models with others.
  arXiv  Detail & Related papers  (2024-03-12T16:46:54Z)
• CP-SLAM: Collaborative Neural Point-based SLAM System [54.916578456416204]
  This paper presents a collaborative implicit neural localization and mapping (SLAM) system with RGB-D image sequences. In order to enable all these modules in a unified framework, we propose a novel neural point based 3D scene representation. A distributed-to-centralized learning strategy is proposed for the collaborative implicit SLAM to improve consistency and cooperation.
  arXiv  Detail & Related papers  (2023-11-14T09:17:15Z)
• RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments [1.1024591739346294]
  We propose a novel visual SLAM system named RWT-SLAM to tackle this problem. We modify LoFTR network which is able to produce dense point matching under low-textured scenes to generate feature descriptors. The resulting RWT-SLAM is tested in various public datasets such as TUM and OpenLORIS.
  arXiv  Detail & Related papers  (2022-07-07T19:24:03Z)
• Content-aware Warping for View Synthesis [110.54435867693203]
  We propose content-aware warping, which adaptively learns the weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from two source views. Experimental results on structured light field datasets with wide baselines and unstructured multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2022-01-22T11:35:05Z)
• NICE-SLAM: Neural Implicit Scalable Encoding for SLAM [112.6093688226293]
  NICE-SLAM is a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust.
  arXiv  Detail & Related papers  (2021-12-22T18:45:44Z)
• DIETERpy: a Python framework for The Dispatch and Investment Evaluation Tool with Endogenous Renewables [62.997667081978825]
  DIETER is an open-source power sector model designed to analyze future settings with very high shares of variable renewable energy sources. It minimizes overall system costs, including fixed and variable costs of various generation, flexibility and sector coupling options. We introduce DIETERpy that builds on the existing model version, written in the General Algebraic Modeling System (GAMS) and enhances it with a Python framework.
  arXiv  Detail & Related papers  (2020-10-02T09:27:33Z)
• Redesigning SLAM for Arbitrary Multi-Camera Systems [51.81798192085111]
  Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. We adapt a state-of-the-art visual-inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups.
  arXiv  Detail & Related papers  (2020-03-04T11:44:42Z)
• OpenVSLAM: A Versatile Visual SLAM Framework [13.268738551141107]
  We introduce OpenVSLAM, a visual SLAM framework with high usability. This software is designed to be easily used and extended. It incorporates several useful features and functions for research and development.
  arXiv  Detail & Related papers  (2019-10-02T18:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.