论文标题
一种嵌入式的单眼视觉方法，用于地面感知对象检测和位置估计
An Embedded Monocular Vision Approach for Ground-Aware Objects Detection and Position Estimation
论文作者
论文摘要
在Robocup小型联盟（SSL）中，鼓励团队提出解决方案，以便仅使用嵌入式感应信息在SSL字段内执行基本足球任务。因此，这项工作提出了一种嵌入式的单眼视觉方法，用于检测对象并估计足球场内的相对位置。通过假设对象放在地面上，并且板载摄像头的位置固定在机器人上，可以利用来自环境的先验知识。我们在NVIDIA JETSON NANO上实施了提出的方法，并使用SSD Mobilenet V2用于2D对象检测，并通过张力优化，检测球，机器人和目标，距离高达3.5米。球定位评估表明，所提出的解决方案克服了当前使用的SSL视觉系统，该系统的位置超过1米，距离板载摄像头14.37毫米。此外，提出的方法以每秒30帧的平均处理速度实现实时性能。
In the RoboCup Small Size League (SSL), teams are encouraged to propose solutions for executing basic soccer tasks inside the SSL field using only embedded sensing information. Thus, this work proposes an embedded monocular vision approach for detecting objects and estimating relative positions inside the soccer field. Prior knowledge from the environment is exploited by assuming objects lay on the ground, and the onboard camera has its position fixed on the robot. We implemented the proposed method on an NVIDIA Jetson Nano and employed SSD MobileNet v2 for 2D Object Detection with TensorRT optimization, detecting balls, robots, and goals with distances up to 3.5 meters. Ball localization evaluation shows that the proposed solution overcomes the currently used SSL vision system for positions closer than 1 meter to the onboard camera with a Root Mean Square Error of 14.37 millimeters. In addition, the proposed method achieves real-time performance with an average processing speed of 30 frames per second.
