ExtPerFC：一种面向移动协作机器人的高效2D与3D感知软硬件框架
ExtPerFC: An Efficient 2D and 3D Perception Hardware-Software Framework for Mobile Cobot
AI总结：
本文提出ExtPerFC软硬件框架，在无需额外专用GPU的现有移动协作机器人上集成2D检测与3D深度估计，并通过Baxter实验验证其多任务实时感知能力。
摘要：
由于机器人感知的可靠性与为应对不确定性而集成的传感模态数量相关，因此需要一种实用方案，以最小工作量在现有机器人系统上管理来自不同计算机的传感器、使其同时运行并维持实时性能。在本工作中，我们提出一个端到端软硬件框架，即ExtPerFC，它既支持传统硬件和软件组件，又能集成机器学习目标检测器，而无需额外的专用图形处理器（GPU）。我们首先设计该框架，以在现有机器人系统上实现实时性能、保证配置优化并注重代码可复用性。随后，我们对用于2D目标检测的迁移学习策略进行数学建模并加以运用，并将其融合到深度图像中以进行3D深度估计。最后，我们在配备两条7自由度机械臂、一个四轮移动底盘和一台Intel RealSense D435i RGB-D相机的Baxter机器人上系统测试所提出框架。结果表明，机器人借助分布式计算机上的Intel机载CPU/GPU等可用硬件，在同时执行其他任务（例如地图构建、定位、导航、目标检测、机械臂运动和抓取）时实现了实时性能。此外，为全面控制、编程和监控机器人系统，我们设计并推出了一款终端用户应用程序。源代码可在https://github.com/tuantdang/perception_framework获取。
英文摘要：
As the reliability of the robot's perception correlates with the number of integrated sensing modalities to tackle uncertainty, a practical solution to manage these sensors from different computers, operate them simultaneously, and maintain their real-time performance on the existing robotic system with minimal effort is needed. In this work, we present an end-to-end software-hardware framework, namely ExtPerFC, that supports both conventional hardware and software components and integrates machine learning object detectors without requiring an additional dedicated graphic processor unit (GPU). We first design our framework to achieve real-time performance on the existing robotic system, guarantee configuration optimization, and concentrate on code reusability. We then mathematically model and utilize our transfer learning strategies for 2D object detection and fuse them into depth images for 3D depth estimation. Lastly, we systematically test the proposed framework on the Baxter robot with two 7-DOF arms, a four-wheel mobility base, and an Intel RealSense D435i RGB-D camera. The results show that the robot achieves real-time performance while executing other tasks (e.g., map building, localization, navigation, object detection, arm moving, and grasping) simultaneously with available hardware like Intel onboard CPUS/GPUs on distributed computers. Also, to comprehensively control, program, and monitor the robot system, we design and introduce an end-user application. The source code is available at https://github.com/tuantdang/perception_framework.
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。
