Reconfigurable On-Board Vision Processing for Small Autonomous Vehicles

Fife, Wade S.; Archibald, James K.

doi:10.1155/2007/80141

Cited by 21 publications

(11 citation statements)

References 5 publications

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“…The system we have developed implements real-time feature tracking and vision guidance on a custom FPGA board [24] mounted on a small ground UV capable of autonomous operation. The FPGA is the sole computational support on the vehicle, so it performs all processing associated with sensing, communication, and control.…”

Section: Hardware Structurementioning

confidence: 99%

“…Our design was implemented on the Helios board, a custom FPGA-based circuit board design to support research in image-directed navigation and control [24]. Measuring just 6.5cm × 9cm, the Helios board we used includes a Virtex-4 FX60 FPGA, 32 MB of SDRAM, 4 MB of SRAM, 16 MB of flash memory, a USB 2.0 interface, and a 120-pin header for daughter board expansion.…”

Section: Testbenchmentioning

confidence: 99%

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An on-board vision sensor system for small unmanned vehicle applications

Tippetts

Lee

Archibald

2012

Machine Vision and Applications

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This paper describes an on-board vision sensor system that is developed specifically for small unmanned vehicle applications. For small vehicles, vision sensors have many advantages, including size, weight, and power consumption, over other sensors such as radar, sonar, and laser range finder, etc. A vision sensor is also uniquely suited for tasks such as target tracking and recognition that require visual information processing. However, it is difficult to meet the computing needs of real-time vision processing on a small robot. In this paper, we present the development of a field programmable gate array-based vision sensor and use a small ground vehicle to demonstrate that this vision sensor is able to detect and track features on a user-selected target from frame to frame and steer the small autonomous vehicle towards it. The sensor system utilizes hardware implementations of the rank transform for filtering, a Harris corner detector for feature detection, and a correlation algorithm for feature matching and tracking. With additional capabilities supported in software, the operational system communicates wirelessly with a base station, receiving commands, providing visual feedback to the user and allowing user input such as specifying targets to track. Since this vision sensor system uses reconfigurable hardware, other vision algorithms such as stereo vision and motion analysis can be implemented to reconfigure the system for other real-time vision applications.

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Section: Hardware Structurementioning

confidence: 99%

Section: Testbenchmentioning

confidence: 99%

An on-board vision sensor system for small unmanned vehicle applications

Tippetts

Lee

Archibald

2012

Machine Vision and Applications

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“…Scheduling algorithms from the single processor domain are applied to schedule the sequential reconfiguration port of the execution environment. The utilization of reconfigurable hardware for vision processing in small autonomous vehicles is discussed in Fife and Archibald [2007]. FPGAs are used for the implementation of real-time vision algorithms that construct a threedimensional map of the environment surrounding the mobile robot.…”

Section: Dynamic Reconfiguration On Bebotmentioning

confidence: 99%

Applying dynamic reconfiguration in the mobile robotics domain

Nava

Sciuto

Santambrogio

et al. 2011

ACM Trans. Reconfigurable Technol. Syst.

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Mobile robots are widely used in industrial environments and are expected to be widely available in human environments in the near future, for example, in the area of care and service robots. This article proposes an implementation for a highly customizable color recognition module based on Field Programmable Gate Array (FPGA) hardware to accomplish tasks like real-time frame processing for image streams. In comparison to a pure software solution on a CPU, an attached FPGA-based hardware accelerator enables real-time image processing and significantly reduces the required computing power of the CPU. Instead, the CPU can be used for tasks that cannot be efficiently implemented on FPGAs, for example, because of a large control overhead. We concentrate on a multirobot scenario where a group of robots follows a human team member by keeping a specific formation in order to support the human in exploration and object detection. Additionally, the robots provide a communication infrastructure to maintain a stable multihop communication network between the human and a base station recording all actions and evaluating the captured images and transmitted data. Depending on the current operating conditions, the robot system has to be able to execute a wide variety of different tasks. Since only a small number of tasks have to be executed concurrently, dynamic reconfiguration of the FPGA can be used to avoid the parallel implementation of all tasks on the FPGA. Within this context, this article discusses application fields where dynamic reconfiguration of FPGA-based coprocessors significantly reduces the CPU load and presents examples of how dynamic reconfiguration can be used in exploration.

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“…The BYU Helios Robotic Vision board [45], [46] was developed in the Robotic Vision Laboratory at Brigham Young University, Provo, UT, for real-time visual computing. The Helios board, shown in Fig.…”

Section: A Hardware Platform and Test Bench Vehiclementioning

confidence: 99%

Hardware-Friendly Vision Algorithms for Embedded Obstacle Detection Applications

Wei¹,

Lee

Nelson

et al. 2010

IEEE Trans. Circuits Syst. Video Technol.

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Accurate optical flow estimation is a crucial task for many computer vision applications. However, because of its computational power and processing speed requirements, it is rarely used for real-time obstacle detection, especially for small unmanned vehicle and embedded applications. Two hardware-friendly vision algorithms are proposed in this paper to address this challenge. A ridge regression-based optical flow algorithm is developed to cope with the existing collinear problem in traditional least-squares approaches for calculating optical flow. Additionally, taking advantage of hardware parallelism, spatial and temporal smoothing operations are applied to image sequence derivatives to improve accuracy. An efficient motion field analysis algorithm using the optical flow values and based on a simplified motion model is also developed and implemented in hardware. The resulting obstacle detection algorithm is specifically designed for ground vehicles moving on planar surfaces. Results from the software simulations and hardware execution of the two proposed algorithms prove that with adequate hardware, a low power, compact obstacle detection sensor can be realized for small unmanned vehicles and embedded applications.Index Terms-Embedded vision sensor, field programmable gate array (FPGA), motion analysis, obstacle detection, optical flow, unmanned vehicles.

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Reconfigurable On-Board Vision Processing for Small Autonomous Vehicles

Cited by 21 publications

References 5 publications

An on-board vision sensor system for small unmanned vehicle applications

An on-board vision sensor system for small unmanned vehicle applications

Applying dynamic reconfiguration in the mobile robotics domain

Hardware-Friendly Vision Algorithms for Embedded Obstacle Detection Applications

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