This paper presents an autonomous navigation framework for reaching a goal in unknown 3D cluttered environments. The framework consists of three main components. First, a computationally efficient method for mapping the environment from the disparity measurements obtained from a depth sensor. Second, a stochastic method to generate a path to a given goal, taking into account field of view constraints on the space that is assumed to be safe for navigation. Third, a fast method for the online generation of motion plans, taking into account the robot's dynamic constraints, model and environmental uncertainty and disturbances. To highlight the contribution with respect to the available literature, we provide a qualitative and quantitative comparison with state of the art methods for reaching a goal and for exploration in unknown environments, showing the superior performance of our approach. To illustrate the effectiveness of the proposed framework, we present experiments in multiple indoors and outdoors environments running the algorithm fully on board and in real-time, using a robotic platform based on the Intel Ready to Fly drone kit, which represents the implementation in the most frugal platform for navigation in unknown cluttered environments demonstrated to date. See video at this https URL

中文翻译：

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MAV在未知杂乱环境中的自主导航

本文提出了一种自主导航框架，用于在未知的 3D 杂乱环境中实现目标。该框架由三个主要组件组成。首先，一种从深度传感器获得的视差测量值映射环境的计算有效方法。其次，一种随机方法来生成到给定目标的路径，考虑到对导航安全的空间的视野限制。第三，一种在线生成运动计划的快速方法，考虑到机器人的动态约束、模型和环境的不确定性和干扰。为了突出对可用文献的贡献，我们提供了与最先进的方法的定性和定量比较，以实现目标和在未知环境中进行探索，展示了我们方法的卓越性能。为了说明所提出框架的有效性，我们使用基于英特尔 Ready to Fly 无人机套件的机器人平台在多个室内和室外环境中进行了实验，该环境完全在机上实时运行该算法，该平台代表了迄今为止在未知的杂乱环境中展示的最节俭的导航平台。在此 https URL 观看视频 它代表了迄今为止展示的在未知杂乱环境中最节俭的导航平台的实现。在此 https URL 观看视频 它代表了迄今为止展示的在未知杂乱环境中最节俭的导航平台的实现。在此 https URL 观看视频