CMU-GPR Dataset: Ground Penetrating Radar Dataset for Robot Localization and Mapping
This provides a resource for researchers in robotics and perception to explore subsurface-aided navigation, but it is incremental as it primarily introduces a new dataset.
The authors tackled the lack of ground penetrating radar (GPR) datasets for robot navigation by creating the CMU-GPR dataset, which includes 15 trajectory sequences in GPS-denied indoor environments with ground truth positions and utility code for data processing.
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.