用地面图像实现厘米级机器人定位，无需标记物。
Robot Localization Using a Learned Keypoint Detector and Descriptor with a Floor Camera and a Feature Rich Industrial Floor
- 基于深度网络从工业地面提取特征点进行定位
- 75.7%图像定位准确，平均位置误差2厘米，旋转误差2.4度
- 无需滤波或时序信息，适合实时移动场景
机器人的定位依赖环境中的有效特征。尽管激光雷达系统广泛应用，但地面图像也可提取独特特征。本文提出关键点定位框架KOALA，利用深度神经网络从工业地板中提取足够特征，实现无标记物的高精度定位。所用地板覆盖材料成本与普通工业地板相当。即使不使用任何滤波、先验或时序信息，仍可在75.7%的图像中实现平均位置误差2厘米、旋转误差2.4度的定位精度。因此，机器人“丢失”问题可实现每帧高精度解决，即使在移动过程中亦然。此外，本框架的检测器与描述子组合优于现有方法。
原文摘要 · Abstract (English)
The localization of moving robots depends on the availability of good features from the environment. Sensor systems like Lidar are popular, but unique features can also be extracted from images of the ground. This work presents the Keypoint Localization Framework (KOALA), which utilizes deep neural networks that extract sufficient features from an industrial floor for accurate localization without having readable markers. For this purpose, we use a floor covering that can be produced as cheaply as common industrial floors. Although we do not use any filtering, prior, or temporal information, we can estimate our position in 75.7 % of all images with a mean position error of 2 cm and a rotation error of 2.4 %. Thus, the robot kidnapping problem can be solved with high precision in every frame, even while the robot is moving. Furthermore, we show that our framework with our detector and descriptor combination is able to outperform comparable approaches.
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