Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR's existing scientific use cases.
DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads
School of Information Science and Technology, Northwest University（信息科学与技术学院，西北大学）；Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）
Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar
School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机科学学院）；TopXGun Robotics（TopXGun机器人）；CARDC；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子信息工程学院）
STONE Dataset: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation
Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能交叉学科项目，首尔国立大学）；Department of Electrical and Computer Engineering, Seoul National University（电气与计算机工程系，首尔国立大学）
Multi-stage Planning for Multi-target Surveillance using Aircrafts Equipped with Synthetic Aperture Radars Aware of Target Visibility
Grupo de Tratamiento de Imágenes, Information Processing and Telecommunications Center, ETSI Telecomunicación, Universidad Politécnica de Madrid（图像处理与电信中心，理工大学马德里大学）；Airbus Defence and Space（空客防务与航天）
本文提出多阶段规划系统，通过 waypoint 序列规划、神经网络预测最佳可见性飞行段及优化连接生成轨迹，实现多目标 SAR 监视的高质量成像与实时性能。
A multi-robot system for the detection of explosive devices
Royal Military Academy of Belgium（比利时皇家军事学院）；Advanced Center for Aerospace Technologies (CATEC)（航空航天技术高级中心）；Space Applications Services NV/SA（空间应用服务公司）；Lightnovo ApS（莱特诺沃公司）；Scientific Council HCR—Center for Testing, Development, and Training（HCR科学委员会测试、开发与培训中心）
