动态开放词汇增强的智能安全着陆（DOVESEI）
Dynamic Open Vocabulary Enhanced Safe-landing with Intelligence (DOVESEI)
AI总结：
本文提出DOVESEI框架，利用开放词汇图像分割实现无人机从100米至20米高度的安全着陆，并引入动态聚焦掩码机制解决帧间分割波动问题，使着陆成功率较全局分割提升近十倍。
摘要：
这项工作针对我们认为是城市空中机器人的基础步骤——安全着陆。我们的注意力集中在安全着陆感知技术栈中最关键的方面：分割。我们提出了一种简化的反应式无人机系统，该系统利用开放词汇图像分割的能力进行视觉伺服。得益于其开放词汇方法，这种方法可以通过最少的调整适应各种场景，无需积累大量数据来完善内部模型。鉴于地方当局施加的限制，我们的主要关注点集中在从100米高度开始的作业。这一选择是经过深思熟虑的，因为许多先前的工作已经处理了高达30米的高度，这与小型立体相机的能力相一致。因此，我们将剩余的20米留给常规3D路径规划方法进行导航。利用单目相机和图像分割，我们的研究结果表明，该系统能够在低至20米的高度成功执行着陆机动。然而，这种方法容易受到视频流中帧间分割间歇性甚至突然波动的影响。为了应对这一挑战，我们通过引入所谓的动态聚焦来增强图像分割输出：一种根据当前着陆阶段进行自我调整的掩码机制。这种动态聚焦引导控制系统避开超出无人机在地面上投影安全半径的区域，从而缓解了波动带来的问题。通过实施这一补充层，与全局分割相比，我们的实验在着陆成功率上取得了近十倍的提升。所有源代码均已开源并在线提供（github.com/MISTLab/DOVESEI）。
英文摘要：
This work targets what we consider to be the foundational step for urban airborne robots, a safe landing. Our attention is directed toward what we deem the most crucial aspect of the safe landing perception stack: segmentation. We present a streamlined reactive UAV system that employs visual servoing by harnessing the capabilities of open vocabulary image segmentation. This approach can adapt to various scenarios with minimal adjustments, bypassing the necessity for extensive data accumulation for refining internal models, thanks to its open vocabulary methodology. Given the limitations imposed by local authorities, our primary focus centers on operations originating from altitudes of 100 meters. This choice is deliberate, as numerous preceding works have dealt with altitudes up to 30 meters, aligning with the capabilities of small stereo cameras. Consequently, we leave the remaining 20m to be navigated using conventional 3D path planning methods. Utilizing monocular cameras and image segmentation, our findings demonstrate the system's capability to successfully execute landing maneuvers at altitudes as low as 20 meters. However, this approach is vulnerable to intermittent and occasionally abrupt fluctuations in the segmentation between frames in a video stream. To address this challenge, we enhance the image segmentation output by introducing what we call a dynamic focus: a masking mechanism that self adjusts according to the current landing stage. This dynamic focus guides the control system to avoid regions beyond the drone's safety radius projected onto the ground, thus mitigating the problems with fluctuations. Through the implementation of this supplementary layer, our experiments have reached improvements in the landing success rate of almost tenfold when compared to global segmentation. All the source code is open source and available online (github.com/MISTLab/DOVESEI).
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。
