Object Detection and Mapping During European Robotic Competitions - Lesson Learned

K. Majek, J. Będkowski, M. Pelka, Jakub Ratajczak, A. Maslowski
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Abstract

This paper describes the approach to three European robotic competitions ERL 2017 Major Tournament, ERL 2018 Local Tournament and ELROB 2018. In all of the competitions, GPU enabled SLAM was used to deliver the 3D map of the environment during the mission. In both ERL competitions, deep neural networks were used to identify objects of potential interest. Datasets used to train models and architectures of neural networks are described. All of the object detection models used during the competitions are published in a publicly available repository11https://github.com/karolmajek/ERL2017-ERL2018-Emergency-Object-Detection.
欧洲机器人比赛中的目标检测和映射-经验教训
本文介绍了三场欧洲机器人比赛ERL 2017 Major Tournament、ERL 2018 Local Tournament和ELROB 2018的方法。在所有的比赛中,GPU支持的SLAM被用于在任务期间提供环境的3D地图。在两个ERL竞赛中,深度神经网络被用来识别潜在的兴趣对象。描述了用于训练模型和神经网络架构的数据集。竞赛期间使用的所有目标检测模型都发布在一个公开可用的存储库中11https://github.com/karolmajek/ERL2017-ERL2018-Emergency-Object-Detection。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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