CMU-GPR Dataset: Ground Penetrating Radar Dataset for Robot Localization and Mapping
Alexander Baikovitz, Paloma Sodhi, Michael Dille, Michael Kaess

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…
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Taxonomy
TopicsGeophysical Methods and Applications · Indoor and Outdoor Localization Technologies · Robotics and Sensor-Based Localization
