Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailable. In this work, we propose an image-based local representation of the environment immediately around a robot to parse navigability. We further develop a local planning and control framework, a Pareto-optimal mapless visual navigator (POVNav), to use this representation and enable autonomous navigation in various challenging and real-world environments. In POVNav, we choose a Pareto-optimal sub-goal in the image by evaluating all the navigable pixels, finding a safe visual path, and generating actions to follow the path using visual servo control. In addition to providing collision-free motion, our approach enables selective navigation behavior, such as restricting navigation to select terrain types, by only changing the navigability definition in the local representation. The ability of POVNav to navigate a robot to the goal using only a monocular camera without relying on a map makes it computationally light and easy to implement on various robotic platforms. Real-world experiments in diverse challenging environments, ranging from structured indoor environments to unstructured outdoor environments such as forest trails and roads after a heavy snowfall, using various image segmentation techniques demonstrate the remarkable efficacy of our proposed framework.

翻译：无地图导航已成为一种有前景的方法，使自主机器人能够在预先存在的地图可能不准确、过时或不可用的环境中导航。本文提出了一种基于图像的局部环境表示方法，用于解析机器人周围环境的可通行性。我们进一步开发了一个局部规划与控制框架——帕累托最优无地图视觉导航器（POVNav），利用该表示法在多种具有挑战性的真实环境中实现自主导航。在POVNav中，我们通过评估所有可通行像素、寻找安全视觉路径并利用视觉伺服控制生成跟随路径的动作，在图像中选择一个帕累托最优子目标。除了提供无碰撞运动外，我们的方法还能通过仅改变局部表示中的可通行性定义，实现选择性导航行为（例如将导航限制在特定地形类型上）。POVNav仅依靠单目摄像头即可在无需地图的情况下引导机器人到达目标，使其计算复杂度低且易于在多种机器人平台上实现。在从结构化室内环境到非结构化户外环境（如森林小径和暴雪后的道路）等多种具有挑战性的真实场景中，使用不同图像分割技术进行的实验展示了我们提出的框架的显著有效性。