Performance of Single Board Computers for Vision Processing
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United States Military Academy USMA Digital Commons West Point Research Papers Winter 1-29-2021 Performance of Single Board Computers for Vision Processing Curtis Manore CDT'21 United States Military Academy Pratheek Manjunath United States Military Academy, [email protected] Dominic Larkin United States Military Academy Follow this and additional works at: https://digitalcommons.usmalibrary.org/usma_research_papers Part of the Computational Engineering Commons, and the Other Computer Engineering Commons Recommended Citation Manore, Curtis CDT'21; Manjunath, Pratheek; and Larkin, Dominic, "Performance of Single Board Computers for Vision Processing" (2021). West Point Research Papers. 378. https://digitalcommons.usmalibrary.org/usma_research_papers/378 This Conference Proceeding is brought to you for free and open access by USMA Digital Commons. It has been accepted for inclusion in West Point Research Papers by an authorized administrator of USMA Digital Commons. For more information, please contact [email protected]. Performance of Single Board Computers for Vision Processing Curtis Manore Pratheek Manjunath Dominic Larkin United States Military Academy United States Military Academy United States Military Academy West Point, New York West Point, New York West Point, New York [email protected] [email protected] [email protected] Abstract—With the increasing complexity of machine vision accelerator to aid in vision processing is a dedicated graphics algorithms and growing applications of image processing, how processing unit (GPU), which is specifically designed for do computers without a dedicated graphics processor perform? image processing. The GPU’s ability to perform fast matrix This research discusses the computational abilities of two low- cost single board computers (SBCs) by subjecting them to various computations in parallel makes it useful for processing multi- Visual Inertial Odometry (VIO) algorithms. The end goal of this dimensional arrays representing pixel data [3]. While GPUs research is to identify a SBC which meets the requirements of require high power consumption, recent developments in SBCs being employed on an Unmanned Aerial System for autonomous brought GPUs to the mobile computing arena. Current SBCs navigation. utilize integrated and dedicated GPUs to aid in vision process- Keywords: Single Board Computer, Odroid, Computer Vision, VIO ing while remaining a low-cost, mobile platform. I. INTRODUCTION A. Background The advent of a credit card sized computer, the Rasp- Research and development of Small Unmanned Aerial berry Pi, has revolutionized the concept of the Internet of Systems (sUAS) has greatly benefited from the capabilities Things (IoT) [1]. This class of devices, commonly known brought on by SBCs. Most unmanned systems need to estimate as Single Board Computers, are often used as embedded their position and orientation to autonomously navigate in 3D computers by academics, industry professionals, and hobbyists space. In environments where external references (such as for applied research and tinkering. A single-board computer GNSS) are contested, inaccurate, or unavailable [4], Visual (SBC) is a device with all components such as CPU, RAM, Inertial Odometry (VIO) has emerged as a reliable alterna- storage, and network card mounted on one circuit board. tive. VIO algorithms help aid in navigation by fusing com- Hence they have a considerably smaller footprint than their puter vision and inertial measurements. These algorithms use desktop counterparts. SBCs have become a staple component monocular or stereo sensor input from a camera and provide of mobile robots. This can be attributed to their low-cost, state estimation of the robot’s position and orientation. The light-weight characteristics, and more recently – their proven architecture of most VIO algorithms is separated into two integration with the Robot Operating System (ROS). Due to parts: a front-end and a back-end. The front-end is responsible advances in software development and efficiencies gained in for processing the vision-based aspects of the algorithm. The vision processing algorithms, these embedded computers that vision processing here involves detecting, tracking, and vali- often lack the computational power of most laptops are now dating of landmarks observed from each image frame provided capable of providing the needs of computer vision for mobile by the sensor. The front-end algorithm creates landmarks from platforms. everyday objects or noticeable features in an image, such as Machine vision, also known as computer vision, involves the window corners, door frames, and table edges. Once the acquiring, processing and analyzing visual data to produce front-end identifies and validates these features, it compares an understanding of the real world as perceived by human the current features to their locations in the previous image to eyesight. Through the use of optical sensors (such as cameras) arrive at an initial state estimation for the UAS [5]. Since and algorithms, computers are able to recognize shapes, track an image contains many features, the back-end applies a motion, detect objects and estimate the pose of a body [2]. The smoothing algorithm to minimize the error between the state algorithms that serve vision processing can be computationally estimation and actual pose of the robot through methods such intensive and average CPUs in personal computers may be as a maximum a posteriori (MAP) optimization process [6]. insufficient to support computer vision. A common hardware After this stage, filtering or Inertial Measurement Unit (IMU) pre-integration takes place to improve the pose estimate. [6]. This work is supported by the Army Research Laboratory. The views and With filtering processes such as an Extended Kalman Filter conclusions contained herein are those of the authors and should not be (EKF) solving non-linear system for state estimation in the interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Department of Defense or the U.S. front-end and MAP optimization for smoothing in the back- Government. end, a powerful processing component is needed to handle U.S. Government work not protected by U.S. copyright the VIO algorithm’s demands. However, one needs to be 1) Processor: Clock frequency and number of processor cognizant of the computing device’s size, weight and power cores are key factors affecting the rate at which an SBC consumption when the robotic platform is an aerial system. can conduct computations. Greater number of cores increase These constraints often limit the amount of computational parallel processing capabilities and higher clock frequencies capability available on an sUAS. increase the number of instructions per second that the CPU can process, thereby leading to an increase in performance. II. RELATED WORK &LITERATURE SURVEY 2) RAM: VIO algorithms may need to allocate a large Previous attempts at comparing Single Board Computers amount of information to memory. Hence the most important have looked at processor architecture or operating system’s consideration of RAM for vision processing is simply the total kernel [7]. Research and evaluation of SBCs are typically amount of RAM. Second factor affecting memory utilization performed for one’s specific application, like with the case is the read/write speed of the chipset. DDR5 is the fastest of Hadoop Distributed File System [8] or for parallel discrete- version of SDRAM currently available but DDR4 has greater event simulation of telecommunication systems [9]. Multiple prevalence and compatibility. SBCs can also be combined in computing clusters for im- 3) Storage Media Type: SBCs can have different media proved parallel processing [10]. types and interfaces for storage. Embedded Multi-Media Card Artificial intelligence—a relatively new era in mobile (eMMC), Secure Digital (SD) card, and Serial Advanced robotics—is being implemented with Single Board Computers Technology Attachment (SATA) Solid State Drives (SSD) are to conduct modeling and planning techniques, low level con- commonly used. NVMe SSDs are still nascent in the field trol, and state estimation [11]. One of the less powerful SBCs of SBCs. eMMC and SD cards are flash storage interfaces used in this research is the Odroid XU4. Sensor fusion and whereas SSDs use SATA. While the SATA interface has faster robot navigation using natively available SLAM algorithms in read and write speeds, an SSD takes up more physical space ROS have been successfully implemented on a small ground than flash media, causing a disadvantage for use on an aerial robot with the Odroid XU4 [12]. platform. Many computer enthusiasts like to test the limits of their 4) Cost: A low-cost device makes the system more ex- hardware through the use of benchmarking tools such as pendable. If the sUAS were to crash or if the SBC were 3DMark [13]. to get damaged during prototype testing, there would not be One of the problems with using benchmarking tools is that significant budget setbacks to the project. they do not always model a computer system’s actual usage 5) Power: The peak power consumption and operating by the end-user. They mostly test only one or two aspects of a voltage feature as one of the selection criteria in the table. computer’s performance in isolation. Users of SBC have very Since the SBC would be powered from the battery on a sUAS, diverse use cases for these small computers, and we would be lesser power consumed by this payload translates to longer remiss by subjecting them to just one benchmark. We attempt flight time. to take into account several factors that might influence how 6) Size and weight: Size and weight play a major role in the SBC performs. Some variables include specific versions of the implementation of a computing device on a sUAS. Light software, libraries, device drivers and compiler options used. weight devices are always preferred for increased endurance. This research covers a breadth of characteristics for analyz- Smaller form factor devices are easier to mount and secure on ing various SBCs for the sole purpose of performing Visual sUAS frames.