Challenges and implemented technologies used in autonomous drone racing
Challenges and implemented technologies used in autonomous drone racing
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DOI：
10.1007/s11370-018-00271-6
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发表时间：
2019-04-01
影响因子：
2.5
通讯作者：
Kim, Si Jung
中科院分区：
文献类型：
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作者：
Moon, Hyungpil;Martinez-Carranza, Jose;Kim, Si Jung
自主无人机竞赛（ADR）是自主无人机在不依赖任何外部感测的情况下在杂乱的室内环境中导航的一个挑战，其中所有的感测和计算都必须通过机载资源来完成。虽然到目前为止还没有团队能够完成整个赛道，但大多数成功的团队都实施了路点跟踪方法，并对不同颜色的门进行了强大的视觉识别，因为完整的环境信息在活动之前就已提供给参与者。在本文中，我们介绍了ADR作为自主无人机技术基准测试场的目的，并分析了在IROS 2016和IROS 2017举行的前两次ADR中使用的挑战和技术。参加这些活动的五个团队展示了他们实施的技术，包括改进的ORB-SLAM，用于路点部署的鲁棒对准方法，用于运动估计的传感器融合，用于门检测和运动控制的深度学习，以及用于门检测的立体视觉。
Autonomous drone racing (ADR) is a challenge for autonomous drones to navigate a cluttered indoor environment without relying on any external sensing in which all the sensing and computing must be done with onboard resources. Although no team could complete the whole racing track so far, most successful teams implemented waypoint tracking methods and robust visual recognition of the gates of distinct colors because the complete environmental information was given to participants before the events. In this paper, we introduce the purpose of ADR as a benchmark testing ground for autonomous drone technologies and analyze challenges and technologies used in the two previous ADRs held in IROS 2016 and IROS 2017. Five teams which participated in these events present their implemented technologies that cover modified ORB-SLAM, robust alignment method for waypoints deployment, sensor fusion for motion estimation, deep learning for gate detection and motion control, and stereo-vision for gate detection.