i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation and Mapping
Hailiang Tang, Tisheng Zhang, Liqiang Wang, Xin Ding, Man Yuan, Zhiyu Xiang, Jujin Chen, Yuhan Bian, Shuangyan Liu, Yuqing Wang, Guan Wang, and Xiaoji Niu

TL;DR
i2Nav-Robot is a comprehensive large-scale dataset featuring multi-sensor data for indoor-outdoor navigation and mapping, designed to advance autonomous ground vehicle research.
Contribution
The paper introduces i2Nav-Robot, a novel dataset with synchronized multi-modal sensors, diverse scenarios, and high-precision ground truth for improved navigation and mapping techniques.
Findings
Dataset includes 10 sequences covering 17,060 meters.
High-accuracy ground truth derived from integrated navigation methods.
Proven superior data quality through evaluation with multiple systems.
Abstract
Accurate and reliable navigation is crucial for autonomous unmanned ground vehicle (UGV). However, current UGV datasets fall short in meeting the demands for advancing navigation and mapping techniques due to limitations in sensor configuration, time synchronization, ground truth, and scenario diversity. To address these challenges, we present i2Nav-Robot, a large-scale dataset designed for multi-sensor fusion navigation and mapping in indoor-outdoor environments. We integrate multi-modal sensors, including the newest front-view and 360-degree solid-state LiDARs, 4-dimensional (4D) radar, stereo cameras, odometer, global navigation satellite system (GNSS) receiver, and inertial measurement units (IMU) on an omnidirectional wheeled robot. Accurate timestamps are obtained through both online hardware synchronization and offline calibration for all sensors. The dataset includes ten…
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