{"title":"Camera-based mapping in search-and-rescue via flying and ground robot teams","authors":"Bernardo Esteves Henriques, Mirko Baglioni, Anahita Jamshidnejad","doi":"10.1007/s00138-024-01594-4","DOIUrl":null,"url":null,"abstract":"<p>Search and rescue (SaR) is challenging, due to the unknown environmental situation after disasters occur. Robotics has become indispensable for precise mapping of the environment and for locating the victims. Combining flying and ground robots more effectively serves this purpose, due to their complementary features in terms of viewpoint and maneuvering. To this end, a novel, cost-effective framework for mapping unknown environments is introduced that leverages You Only Look Once and video streams transmitted by a ground and a flying robot. The integrated mapping approach is for performing three crucial SaR tasks: localizing the victims, i.e., determining their position in the environment and their body pose, tracking the moving victims, and providing a map of the ground elevation that assists both the ground robot and the SaR crew in navigating the SaR environment. In real-life experiments at the CyberZoo of the Delft University of Technology, the framework proved very effective and precise for all these tasks, particularly in occluded and complex environments.\n</p>","PeriodicalId":51116,"journal":{"name":"Machine Vision and Applications","volume":"11 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Vision and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00138-024-01594-4","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Search and rescue (SaR) is challenging, due to the unknown environmental situation after disasters occur. Robotics has become indispensable for precise mapping of the environment and for locating the victims. Combining flying and ground robots more effectively serves this purpose, due to their complementary features in terms of viewpoint and maneuvering. To this end, a novel, cost-effective framework for mapping unknown environments is introduced that leverages You Only Look Once and video streams transmitted by a ground and a flying robot. The integrated mapping approach is for performing three crucial SaR tasks: localizing the victims, i.e., determining their position in the environment and their body pose, tracking the moving victims, and providing a map of the ground elevation that assists both the ground robot and the SaR crew in navigating the SaR environment. In real-life experiments at the CyberZoo of the Delft University of Technology, the framework proved very effective and precise for all these tasks, particularly in occluded and complex environments.
由于灾害发生后的环境状况未知,搜救工作极具挑战性。机器人技术已成为精确绘制环境地图和定位受害者不可或缺的工具。由于飞行机器人和地面机器人在视角和操纵方面具有互补性,因此它们的结合能更有效地实现这一目的。为此,我们介绍了一种新颖、经济高效的未知环境绘图框架,该框架利用了 "只看一次 "以及地面机器人和飞行机器人传输的视频流。这种集成绘图方法用于执行三项关键的 SaR 任务:定位受害者,即确定他们在环境中的位置和身体姿势;跟踪移动的受害者;提供地面高程图,以协助地面机器人和 SaR 人员在 SaR 环境中导航。在代尔夫特理工大学网络动物园(CyberZoo)的实际实验中,该框架被证明对所有这些任务都非常有效和精确,尤其是在隐蔽和复杂的环境中。
期刊介绍:
Machine Vision and Applications publishes high-quality technical contributions in machine vision research and development. Specifically, the editors encourage submittals in all applications and engineering aspects of image-related computing. In particular, original contributions dealing with scientific, commercial, industrial, military, and biomedical applications of machine vision, are all within the scope of the journal.
Particular emphasis is placed on engineering and technology aspects of image processing and computer vision.
The following aspects of machine vision applications are of interest: algorithms, architectures, VLSI implementations, AI techniques and expert systems for machine vision, front-end sensing, multidimensional and multisensor machine vision, real-time techniques, image databases, virtual reality and visualization. Papers must include a significant experimental validation component.