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i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation

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arxiv 2508.11485 v3 pith:EOYZLHOE submitted 2025-08-15 cs.RO

i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation

Hailiang Tang , Tisheng Zhang , Liqiang Wang , Xin Ding , Man Yuan , Zhiyu Xiang , Jujin Chen , Yuhan Bian
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Shuangyan Liu Yuqing Wang Guan Wang Xiaoji Niu
This is my paper
classification cs.RO
keywords navigationdataseti2nav-robotfusiongroundlarge-scalemulti-sensoraccurate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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).

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Cited by 3 Pith papers

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  1. EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness

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    An imitation-learning navigation model conditioned on the robot's body dimensions reduces collisions and improves success across embodiments, using pseudo-labeled internet video pretraining and risk-augmented fine-tuning.

  2. MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry

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    MosaicIMU is a carrier-conditioned MoE pretraining-and-adaptation framework for generalizable neural inertial odometry that adapts to new carriers with lightweight experts and reports 40% and 34% reductions in ATE and...

  3. WinTA-GIL: Windowed Trajectory Alignment for GNSS-IMU-LiDAR Heading Refinement in Intermittent Signal Environments

    cs.RO 2026-07 conditional novelty 5.0

    Windowed rigid registration of LIO trajectories against quality-filtered GNSS points, gated by motion-geometry consistency, yields repeatable heading corrections that cut post-outage drift versus prior fusion baselines.