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

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 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.