We encounter a world where the role of robots has increased in society. Day by day, we can observe that problems related to autonomous vehicles are being solved. The focused problem that we have recently confronted is based on collaborative mapping with multi-robots since collaboratively generated map provides more information about the environment than with a single robot. Moreover, the mapping framework for robot team can be more efficient if 3D laser sensors are placed to cover wider area. However, laser sensors are commonly positioned intuitively and depended mostly on the designer’s choice. To overcome designer-independent laser sensor setup for efficient collaborative mapping, a subjective evaluation of laser sensor placement is developed as another key aspect. This paper proposed a collaborative mapping framework for heterogeneous robot teams to make full use of collected information from different viewpoints and explore large areas more efficiently. It’s comprised of two modules, localization of multi-robots and collaborative map construction. In addition, to avoid arbitrary setup of laser sensors in existing methods whose performance is heavily affected by designers’ experience, a metric evaluation system was defined to determine the optimal placements of laser sensors and achieve maximum sweep area. The proposed methods were verified on both simulation and experiments which were carried out on a heterogeneous robot team (including an unmanned aerial vehicle and a ground vehicle). According to the metric evaluation, best placements were chosen for the laser sensors, and presented collaborative mapping technique was shown to work more efficiently compared with existing methods.

中文翻译：

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异构机器人团队的3D LiDAR传感器设置评估

我们遇到了一个机器人在社会中日益重要的世界。我们每天都可以观察到与自动驾驶汽车有关的问题正在得到解决。我们最近面临的重点问题是基于与多机器人的协作映射，因为与单个机器人相比，协作生成的地图可提供有关环境的更多信息。此外，如果将3D激光传感器放置在更大的区域，则机器人团队的制图框架可能会更加高效。但是，激光传感器通常直观地定位，并且主要取决于设计者的选择。为了克服与设计者无关的激光传感器设置以获得有效的协作映射，对激光传感器放置的主观评估已被开发为另一个关键方面。本文提出了一个针对异构机器人团队的协作映射框架，以充分利用从不同角度收集的信息，并更有效地探索大型区域。它由两个模块组成，即多机器人本地化和协作地图构建。此外，为避免在现有方法中随意设置激光传感器，而现有方法的性能会严重影响设计人员的经验，因此定义了一种度量评估系统，以确定激光传感器的最佳位置并实现最大扫掠面积。通过仿真和实验对所提出的方法进行了验证，这些实验是在异构机器人团队（包括无人飞行器和地面飞行器）上进行的。根据指标评估，为激光传感器选择了最佳位置，