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

2025-08-15

AI Summary

Existing UGV datasets suffer from inadequate sensor configurations, poor temporal synchronization, insufficient ground-truth accuracy, and limited scene diversity, hindering advances in navigation and mapping algorithms. To address these limitations, this work introduces the first multi-sensor synchronized dataset supporting both indoor and outdoor complex environments. It integrates solid-state LiDAR, 4D imaging radar, stereo cameras, and navigation-grade IMU, achieving hardware-level time synchronization and centimeter-accurate, high-frequency ground-truth trajectories via post-processed integrated navigation. The dataset comprises 10 long sequences (totaling 17.06 km), covering diverse scenarios including urban streets and indoor parking garages. An online synchronization and offline joint calibration strategy ensures sensor alignment, and consistency and reliability are validated across more than ten open-source sensor fusion systems. This dataset establishes a high-quality benchmark for research in high-precision localization and multimodal mapping.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

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 comprises ten larger-scale sequences covering diverse UGV operating scenarios, such as outdoor streets, and indoor parking lots, with a total length of about 17060 meters. High-frequency ground truth, with centimeter-level accuracy for position, is derived from post-processing integrated navigation methods using a navigation-grade IMU. The proposed i2Nav-Robot dataset is evaluated by more than ten open-sourced multi-sensor fusion systems, and it has proven to have superior data quality.

Problem

• Lack of diverse UGV datasets for navigation and mapping
• Insufficient sensor synchronization and ground truth accuracy
• Need for multi-sensor fusion in indoor-outdoor environments

Innovation

• Multi-modal sensor integration for fusion
• Hardware and offline time synchronization
• High-accuracy ground truth post-processing