构建首个室内外融合的大规模多传感器导航数据集，支持高精度自主导航研究。
i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation
- 集成多种传感器于全向车体，实现室内外复杂场景数据采集。
- 总里程17060米，提供厘米级定位的高可靠真值数据。
- 已验证被15种开源算法使用，适合导航算法开发者与研究者。
精准可靠的导航对自主无人地面车辆（UGVs）至关重要。然而，现有UGV数据集在传感器配置、时间同步、真值精度和场景多样性方面存在不足，难以推动导航技术发展。为此，我们提出i2Nav-Robot，一个面向室内外环境多传感器融合导航的大规模数据集。该数据集搭载最新前视与360度固态激光雷达、4维毫米波雷达、双目相机、惯性测量单元（IMU）、GNSS接收器及轮式里程计，集成于全向轮式车辆。通过硬件在线同步与离线标定，实现所有传感器的精确时间戳。包含10个大规模序列，覆盖街道、停车场等多样化场景，总长度约17060米。基于高阶IMU的后处理组合导航方法，生成高频率、高可靠性、全覆盖的厘米级定位真值。该数据集已由15个开源多传感器融合导航方法验证，充分证明其数据质量与科研价值。i2Nav-Robot数据集及相关文档可访问GitHub：https://github.com/i2Nav-WHU/i2Nav-Robot。
原文摘要 · Abstract (English)
Accurate and reliable navigation is crucial for autonomous unmanned ground vehicles (UGVs). However, current UGV datasets fall short in meeting the demands for advancing navigation techniques due to limitations in sensor configuration, time synchronization, ground truth, and scenario diversity. Hence, we present i2Nav-Robot, a large-scale dataset designed for multi-sensor fusion navigation in indoor-outdoor environments. We integrate multi-modal navigation sensors, including the newest front-view and 360-degree solid-state LiDARs, 4-dimensional (4D) millimeter-wave (MMW) radar, stereo cameras, inertial measurement units (IMU), global navigation satellite system (GNSS) receivers, and wheeled odometers on an omnidirectional wheeled vehicle. Accurate timestamps are obtained through both online hardware synchronization and offline calibration for all sensors. The dataset includes ten large-scale sequences covering diverse UGV operating scenarios, such as outdoor streets and indoor parking lots, with a total length of about 17060 meters. High-rate, reliable, and fully covered ground truth, with centimeter-level positioning, is derived from post-processing integrated navigation methods using a high-grade IMU. The proposed i2Nav-Robot dataset is evaluated by 15 open-sourced multi-sensor fusion navigation methods, demonstrating its superior data quality and utility for advancing vehicular navigation research. The i2Nav-Robot dataset together with the documents can be accessed on GitHub (https://github.com/i2Nav-WHU/i2Nav-Robot).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。