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As the reliability of the robot's perception correlates with the number ofintegrated sensing modalities to tackle uncertainty, a practical solution tomanage these sensors from different computers, operate them simultaneously, andmaintain their real-time performance on the existing robotic system withminimal effort is needed. In this work, we present an end-to-endsoftware-hardware framework, namely ExtPerFC, that supports both conventionalhardware and software components and integrates machine learning objectdetectors without requiring an additional dedicated graphic processor unit(GPU). We first design our framework to achieve real-time performance on theexisting robotic system, guarantee configuration optimization, and concentrateon code reusability. We then mathematically model and utilize our transferlearning strategies for 2D object detection and fuse them into depth images for3D depth estimation. Lastly, we systematically test the proposed framework onthe Baxter robot with two 7-DOF arms, a four-wheel mobility base, and an IntelRealSense D435i RGB-D camera. The results show that the robot achievesreal-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 ondistributed computers. Also, to comprehensively control, program, and monitorthe robot system, we design and introduce an end-user application. The sourcecode is available at https://github.com/tuantdang/perception_framework.

ExtPerFC: An Efficient 2D and 3D Perception Hardware-Software Framework for Mobile Cobot