VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets

• URL: http://arxiv.org/abs/2504.04457v1
• Date: Sun, 06 Apr 2025 12:02:19 GMT
• Title: VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets
• Authors: Alejandro Fontan, Tobias Fischer, Javier Civera, Michael Milford,
• Abstract summary: VSLAM-LAB is a unified framework designed to streamline the development, evaluation, and deployment of VSLAM systems.<n>It enables seamless compilation and configuration of VSLAM algorithms, automated dataset downloading and preprocessing, and standardized experiment design, execution, and evaluation.
• Score: 64.57742015099531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Simultaneous Localization and Mapping (VSLAM) research faces significant challenges due to fragmented toolchains, complex system configurations, and inconsistent evaluation methodologies. To address these issues, we present VSLAM-LAB, a unified framework designed to streamline the development, evaluation, and deployment of VSLAM systems. VSLAM-LAB simplifies the entire workflow by enabling seamless compilation and configuration of VSLAM algorithms, automated dataset downloading and preprocessing, and standardized experiment design, execution, and evaluation--all accessible through a single command-line interface. The framework supports a wide range of VSLAM systems and datasets, offering broad compatibility and extendability while promoting reproducibility through consistent evaluation metrics and analysis tools. By reducing implementation complexity and minimizing configuration overhead, VSLAM-LAB empowers researchers to focus on advancing VSLAM methodologies and accelerates progress toward scalable, real-world solutions. We demonstrate the ease with which user-relevant benchmarks can be created: here, we introduce difficulty-level-based categories, but one could envision environment-specific or condition-specific categories.

Related papers

• 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)
• Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation [51.06031200728449]
  We propose a novel framework called mccHRL to provide different levels of temporal abstraction on listwise recommendation. Within the hierarchical framework, the high-level agent studies the evolution of user perception, while the low-level agent produces the item selection policy. Results observe significant performance improvement by our method, compared with several well-known baselines.
  arXiv  Detail & Related papers  (2024-09-11T17:01:06Z)
• UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs [74.1976921342982]
  This paper introduces UltraEval, a user-friendly evaluation framework characterized by its lightweight nature, comprehensiveness, modularity, and efficiency. The resulting composability allows for the free combination of different models, tasks, prompts, benchmarks, and metrics within a unified evaluation workflow.
  arXiv  Detail & Related papers  (2024-04-11T09:17:12Z)
• DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing [13.50980509878613]
  Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning based SLAM systems. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection.
  arXiv  Detail & Related papers  (2024-01-17T12:08:30Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method [0.0]
  We propose to combine the potential of deep learning-based feature descriptors with the traditional geometry-based VSLAM. Experiments conducted on KITTI and Euroc datasets show that deep learning can be used to improve the performance of traditional VSLAM systems.
  arXiv  Detail & Related papers  (2021-03-31T20:35:10Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.