结合局部与全局感知的纳米无人机自主导航
Combining Local and Global Perception for Autonomous Navigation on Nano-UAVs
AI总结：
本文提出一种融合视觉全局感知与深度局部感知的纳米无人机自主导航方法，在复杂场景中实现15次飞行100%成功率。
摘要：
在无人机（UAV）执行自主任务时，一个关键挑战是其在未知环境中的导航能力。本文提出了一种用于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，即作为全局感知，与8x8像素深度图相结合，用于近距离机动，即局部感知。在实地测试中，我们的集成策略凸显了视觉和深度感官信息的互补优势。在包含直线路径、静态障碍物规避和90°转弯的复杂导航场景中，我们在15次飞行中实现了100%的成功率。
英文摘要：
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° turns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
