Reliable perception of targets is crucial for the stable operation of autonomous robots. A widely preferred method is keypoint identification in an image, as it allows direct mapping from raw images to 2D coordinates, facilitating integration with other algorithms like localization and path planning. In this study, we closely examine the design and identification of keypoint patches in cluttered environments, where factors such as blur and shadows can hinder detection. We propose four simple yet distinct designs that consider various scale, rotation and camera projection using a limited number of pixels. Additionally, we customize the Superpoint network to ensure robust detection under various types of image degradation. The effectiveness of our approach is demonstrated through real-world video tests, highlighting potential for vision-based autonomous systems.

翻译：可靠的目标感知对于自主机器人的稳定运行至关重要。一种广泛采用的方法是图像中的关键点识别，因为它允许从原始图像直接映射到二维坐标，便于与定位和路径规划等其他算法集成。在本研究中，我们深入探究了在杂乱环境中关键点斑块的设计与识别问题，其中模糊和阴影等因素可能阻碍检测。我们提出了四种简单而独特的设计，这些设计利用有限数量的像素考虑了多种尺度、旋转和相机投影。此外，我们定制了Superpoint网络，以确保在各种类型的图像退化下实现鲁棒检测。通过真实世界视频测试验证了我们方法的有效性，突显了其在基于视觉的自主系统中的潜力。