燃烧SLAM:热SLAM可以提高烟雾充满环境中的情境意识
Benjamin Ronald van Manen1, Victor Sluiter1, Abeje Yenehun Mersha1
1Smart Mechatronics And RoboTics (SMART) Research Group, Saxion University of Applied Sciences, Ariënsplein 1-300, 7511 JX Enschede, The Netherlands.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
消防员现在可以使用FirebotSLAM,一个新的系统,从热图片创建3D地图,以导航烟的建筑物. 这种机器人技术提高了危险环境中的情境意识,提高了安全性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 像火灾这样的极端环境严重限制了消防员的可见性.
- 无人地面车辆 (UGV) 可以在危险条件下提高情境意识.
- 现有的同时定位和映射 (SLAM) 方法在热图数据方面存在困难.
研究的目的:
- 开发第一个用于机器人测距和3D绘图的算法,仅使用热图像.
- 通过增强机器人感知来提高消防员在火灾事件中的安全性.
- 为了应对SLAM在热成像中强大的特征提取的挑战.
主要方法:
- 提出了FirebotSLAM,这是一个用于热SLAM的新算法.
- 在火灾事件数据集上采用基准特征提取和描述方法.
- 将最好的功能组合集成到一个最先进的视觉SLAM算法中.
主要成果:
- FirebotSLAM计算机器人测距,并从热图中生成3D地图.
- 通过循环关闭检测实现了全局轨迹优化.
- 在真实火灾环境中展示了热SLAM算法的第一个成功测试.
结论:
- FirebotSLAM使机器人能够使用热数据导航和绘制危险的,低可见性环境.
- 开发的算法显著提高了紧急响应人员的局势意识.
- 这项技术验证了热SLAM在现实世界火灾场景中的运行适用性.
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