Multi-cam Multi-map VILO Campus Dataset|自动驾驶数据集|机器人导航数据集

arXiv2024-12-06 更新2024-12-07 收录

自动驾驶

机器人导航

下载链接：

https://github.com/zoeylove/Multi-cam-Multi-map-VILO/tree/main

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资源简介：

Multi-cam Multi-map VILO Campus Dataset是由浙江大学工业控制技术国家重点实验室创建的，旨在验证多摄像头多地图视觉惯性定位系统的高精度和实时性。该数据集包含了一个长达9个月的校园环境数据收集，覆盖面积达265,000平方米，轨迹总长度超过55公里。数据集的创建过程包括多传感器硬件设置和多摄像头同步图像流采集，以应对长期环境变化和视角变化。该数据集主要用于评估和优化视觉惯性定位系统，特别是在自动驾驶和机器人导航领域，以解决实时定位和无漂移反馈的问题。

提供机构：

浙江大学工业控制技术国家重点实验室

创建时间：

2024-12-06

AI搜集汇总

数据集介绍

构建方式

Multi-cam Multi-map VILO Campus Dataset的构建方式体现了对多摄像头和多地图视觉惯性定位系统的深入研究。该数据集通过设计一个多传感器硬件平台，包括多个摄像头和惯性测量单元（IMU），以及外部传感器如激光雷达和GPS，实现了对校园环境长期变化的全面捕捉。数据收集过程中，采用了硬件同步技术，确保了传感器数据的时间一致性。此外，数据集还包含了多次在同一地点采集的数据，以构建多会话数据集，从而验证定位系统的鲁棒性。

特点

Multi-cam Multi-map VILO Campus Dataset的特点在于其多摄像头和多地图的配置，这使得数据集能够支持复杂环境下的视觉惯性定位研究。数据集包含了长时间跨度的校园环境变化，包括季节变化、光照变化和结构变化，这些都为验证定位系统的长期稳定性和鲁棒性提供了丰富的场景。此外，数据集还包含了同步的多摄像头图像流，这有助于提高定位系统的鲁棒性和准确性。

使用方法

Multi-cam Multi-map VILO Campus Dataset的使用方法主要包括数据预处理、系统初始化和实时定位。首先，用户需要对采集的多传感器数据进行预处理，确保数据的时间同步和质量。接着，通过初始化模块将当前视觉-惯性观测与预构建的地图关联，计算当前相机相对于地图框架的姿态。最后，利用在线匹配模块获取地图观测，并将其输入到基于地图的视觉惯性导航系统（VINS）模块中，实现实时、准确和一致的状态估计。

背景与挑战

背景概述

Multi-cam Multi-map VILO Campus Dataset 是由浙江大学工业控制技术国家重点实验室的研究团队于2021年创建的。该数据集的核心研究问题是如何在机器人自主移动中提供实时、因果且无漂移的位置反馈。现有的视觉惯性导航系统（VINS）和同时定位与地图构建（SLAM）系统无法直接集成到机器人的控制回路中，因为VINS在长时间操作中会积累漂移，而SLAM提供的无漂移轨迹是在闭环校正后处理的，是非因果的。为了解决这些问题，研究团队提出了一种多相机多地图视觉惯性定位系统，并设计了一个多相机IMU硬件设置，收集了一个长期挑战性的校园数据集。该数据集的开放源代码促进了社区的发展，并对机器人自主导航领域产生了重要影响。

当前挑战

Multi-cam Multi-map VILO Campus Dataset 面临的挑战主要包括两个方面：一是解决领域问题的挑战，即在图像分类和定位中如何处理长时间操作中的漂移问题；二是数据集构建过程中的挑战，包括如何设计多相机IMU硬件设置以收集高质量的数据，以及如何在长期变化的环境中确保数据的鲁棒性和一致性。此外，数据集还需要解决在不同光照和季节条件下的数据收集问题，以确保数据集的多样性和广泛适用性。

常用场景

经典使用场景

在自主机器人导航领域，Multi-cam Multi-map VILO Campus Dataset 数据集的经典应用场景主要集中在视觉惯性定位（VILO）系统的验证与优化。该数据集通过提供多摄像头和多地图的复杂环境数据，支持研究人员开发和测试能够在长时间运行中提供实时、无漂移位置反馈的定位系统。具体应用包括但不限于自主驾驶系统、物流机器人和非结构化环境中的导航任务。

衍生相关工作

基于 Multi-cam Multi-map VILO Campus Dataset 数据集，研究人员开发了多种改进的视觉惯性定位系统，如多摄像头视觉惯性导航系统（MC-VINS）和多地图视觉惯性定位系统（MM-VILO）。这些系统通过集成多个摄像头和地图，显著提高了定位精度和鲁棒性。此外，该数据集还促进了相关领域的研究，如多传感器融合、实时地图更新和动态环境下的定位技术。

数据集最近研究

相关研究论文

1
Multi-cam Multi-map Visual Inertial Localization: System, Validation and Dataset浙江大学工业控制技术国家重点实验室 · 2024年

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<h1 align="center" style="font-size: 36px;"> <span style="color: #FFD700;">AQCat25 Dataset:</span> Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis </h1> ![datset_schematic](https://cdn-uploads.huggingface.co/production/uploads/67256b7931376d3bacb18de0/W1Orc_AmSgRez5iKH0qjC.jpeg) This repository contains the **AQCat25 dataset**. AQCat25-EV2 models can be accessed [here](https://huggingface.co/SandboxAQ/aqcat25-ev2). The AQCat25 dataset provides a large and diverse collection of **13.5 million** DFT calculation trajectories, encompassing approximately 5K materials and 47K intermediate-catalyst systems. It is designed to complement existing large-scale datasets by providing calculations at **higher fidelity** and including critical **spin-polarized** systems, which are essential for accurately modeling many industrially relevant catalysts. 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It tells you where the frame came from in its source simulation. | `4` (the 5th frame of a specific VASP run) | | `frame_id` | **Unique Dataset Pointer**: This is a new ID created for this specific dataset. It tells you exactly which file (`data.0015.aselmdb`) and which row (`101`) to look in to find the full atomic structure. | `data.0015.aselmdb::101` | --- ## Downloadable Data Archives The full, raw data for each split is available for download in compressed `.tar.gz` archives. The table below provides direct download links. The queryable Parquet files for each split can be loaded directly using the `datasets` library as shown in the "Example Usage" section. The data currently available for download (totaling ~11.1M frames, as listed in the table below) is the initial dataset version (v1.0) released on September 10, 2025. 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Log in via the Command Line:** Open your terminal and run the following command: ```bash hf auth login ``` ### 2.2 Get the Helper Scripts You may copy the scripts directly from this repository, or download them by running the following in your local python environment: ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="SandboxAQ/aqcat25", repo_type="dataset", allow_patterns=["scripts/*", "README.md"], local_dir="./aqcat25" ) ``` This will create a local folder named aqcat25 containing the scripts/ directory. ### 2.3 Download Desired Dataset Splits Data splits may be downloaded directly via the Hugging Face UI, or via the `download_split.py` script (found in `aqcat25/scripts/`). ```bash python aqcat25/scripts/download_split.py --split val_id ``` This will download `val_id.tar.gz` and extract it to a new folder named `aqcat_data/val_id/`. ### 2.4 Query the Dataset Use the `query_aqcat.py` script to filter the dataset and extract the specific atomic structures you need. 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