CMU-GPR Dataset: Ground Penetrating Radar Dataset for Robot Localization and Mapping

TL;DR

This paper introduces the CMU-GPR dataset, a comprehensive collection of ground penetrating radar data for robot navigation in GPS-denied environments, enabling research on subsurface perception and long-term mapping.

Contribution

The paper presents a new open-source GPR dataset with multi-sensor data, utility tools, and methods for subsurface-aware robot localization and mapping in indoor environments.

Findings

Dataset includes 15 trajectories in indoor environments

Provides synchronized GPR, visual, and inertial data

Facilitates research in GPS-denied, underground navigation

Abstract

There has been exciting recent progress in using radar as a sensor for robot navigation due to its increased robustness to varying environmental conditions. However, within these different radar perception systems, ground penetrating radar (GPR) remains under-explored. By measuring structures beneath the ground, GPR can provide stable features that are less variant to ambient weather, scene, and lighting changes, making it a compelling choice for long-term spatio-temporal mapping. In this work, we present the CMU-GPR dataset--an open-source ground penetrating radar dataset for research in subsurface-aided perception for robot navigation. In total, the dataset contains 15 distinct trajectory sequences in 3 GPS-denied, indoor environments. Measurements from a GPR, wheel encoder, RGB camera, and inertial measurement unit were collected with ground truth positions from a robotic total station. In addition to the dataset, we also provide utility code to convert raw GPR data into processed images. This paper describes our recording platform, the data format, utility scripts, and proposed methods for using this data.