Large-Scale LiDAR-Inertial Dataset for Degradation-Robust High-Precision Mapping

TL;DR

This paper presents a large-scale, high-precision LiDAR-Inertial dataset designed to evaluate and improve the robustness of LIO systems in complex real-world environments, facilitating better validation and development.

Contribution

It introduces a comprehensive, real-world LiDAR-Inertial dataset with diverse environments, long trajectories, and high-precision ground truth for robust LIO system evaluation.

Findings

Dataset covers four diverse environments totaling up to 750,000 m².

Includes long trajectories with complex scenes and high-precision ground truth.

Provides a benchmark for assessing LIO system generalization in practical scenarios.

Abstract

This paper introduces a large-scale, high-precision LiDAR-Inertial Odometry (LIO) dataset, aiming to address the insufficient validation of LIO systems in complex real-world scenarios in existing research. The dataset covers four diverse real-world environments spanning 60,000 to 750,000 square meters, collected using a custom backpack-mounted platform equipped with multi-beam LiDAR, an industrial-grade IMU, and RTK-GNSS modules. The dataset includes long trajectories, complex scenes, and high-precision ground truth, generated by fusing SLAM-based optimization with RTK-GNSS anchoring, and validated for trajectory accuracy through the integration of oblique photogrammetry and RTK-GNSS. This dataset provides a comprehensive benchmark for evaluating the generalization ability of LIO systems in practical high-precision mapping scenarios.