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.
翻译:由于雷达在各种环境条件下的强度增强,在将雷达用作机器人导航传感器方面最近取得了令人振奋的进展。然而,在这些不同的雷达感知系统中,地面穿透雷达(GPR)仍然未得到充分探索。测量地下结构,GPR能够提供稳定特征,这些特征与周围天气、场景和照明变化相比较少变异,使它成为长期时空测绘的令人信服的选择。在这项工作中,我们提供了CMU-GPR数据系统-开放源地透析雷达数据集,用于对机器人导航的次表层辅助感知进行研究。总的来说,该数据集包含在3个全球定位系统屏蔽的室内环境中的15个不同的轨迹序列。GPR、轮式编码器、RGB摄像机和惯性测量单元的测量与机器人总站的地面真相位置相匹配。除了数据集外,我们还提供了将原始GPR数据转换为经处理的图像的实用代码。本文描述了我们的记录平台、数据格式、通用脚本以及使用这些数据的拟议方法。