A critical challenge in deploying unmanned aerial vehicles (UAVs) for autonomous tasks is their ability to navigate in an unknown environment. This paper introduces a novel vision-depth fusion approach for autonomous navigation on nano-UAVs. We combine the visual-based PULP-Dronet convolutional neural network for semantic information extraction, i.e., serving as the global perception, with 8x8px depth maps for close-proximity maneuvers, i.e., the local perception. When tested in-field, our integration strategy highlights the complementary strengths of both visual and depth sensory information. We achieve a 100% success rate over 15 flights in a complex navigation scenario, encompassing straight pathways, static obstacle avoidance, and 90{\deg} turns.

翻译：自主无人机在未知环境中执行任务的关键挑战之一是其导航能力。本文提出了一种面向纳米无人机自主导航的新型视觉-深度融合方法。我们将基于视觉的PULP-Dronet卷积神经网络用于语义信息提取（即全局感知），与8×8像素深度图用于近距机动操控（即局部感知）相结合。现场测试表明，我们的融合策略充分彰显了视觉与深度传感器信息的互补优势。在包含直线路径、静态避障及90度转向的复杂导航场景中，该方法在15次飞行测试中实现了100%的成功率。