A 100 $\mu$W 128 $\times$ 64 Pixels Contrast-Based Asynchronous Binary Vision Sensor for Sensor Networks Applications

Gottardi, M.; Massari, Nicola; Jawed, Syed Arsalan

doi:10.1109/jssc.2009.2017000

Cited by 76 publications

(66 citation statements)

References 15 publications

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“…Since the pins of the Beagleboard expansion headers are rated at 1.8 V while the camera requires 3.3 V, a piggy-back voltage level translator has been used to interface the embedded platform with the camera. The light contrast sensor is a 35-μm CMOS imager with a resolution of 128 × 64 pixels made at the "Fondazione Bruno Kessler" (FBK), Trento, Italy [43]. Note that the low resolution of the camera is helpful for the application considered, as it greatly reduces the number of iterations of the RANSAC algorithm at no cost in terms of accuracy, as it will be shown in Section 4.2.…”

Section: Experimental Setup Descriptionmentioning

confidence: 99%

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

2015

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Localization and tracking systems are nowadays a quite common solution for automated guided vehicles (AGV) in industrial environments. The bunch of technological solutions developed in this sector is now being revitalized by their applications in service robots, e.g. for safe navigation in crowded public spaces or in autonomous cars. Related to this field are robotic competitions and races. In this particular scenario, the robot often has to track a line painted on the ground. Line tracking techniques typically rely on light dependent resistors (LDR), photo-diodes or photo-transistors detecting the light generated by normal or infrared Light Emitting Diodes (LEDs), arrays of electric inductance sensors or vision systems. Crucial issues for line tracking are: accuracy and reliability in estimating the direction of the moving vehicle with respect to the line and processing speed. Such problems are particularly critical when high-speed mobile robots are considered. To address this issue, in this paper a vision-based technique of moderate computational complexity is described.Â The proposed solution has been implemented on a low-cost embedded platform and relies on a high frame rate light contrast sensor, a tailored RANSAC-based algorithm and a Kalman filter. The reported experimental results prove that the proposed solution is able to track the direction of the vehicle in real-time even when the field of view of the camera is limited and the vehicle moves at high speed.

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Section: Experimental Setup Descriptionmentioning

confidence: 99%

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

2015

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“…The camera consists of a special light contrast imager driven by a Xilinx CPLD Kelly board XEM 3001v2 equipped with an FPGA Xilinx Spartan-3 CoolRunner-II. The imager is a 35-µm CMOS sensor with a resolution of 128 × 64 pixels, which was designed and manufactured at FBK, Trento [19]. The second BeagleBoard is connected to a standard webcam through a USB connection, and is used for trajectory planning.…”

Section: A Hardware Architecturementioning

confidence: 99%

“…Both issues discourage the application of standard visual sensors in our context. In order to build a solution that stays within our cost constraints, we advocate the use of a special camera [19] that produces a low resolution B/W stream. However, the automated edge detection makes it possible to use it with a very aggressive sampling time.…”

Section: Introductionmentioning

confidence: 99%

High speed robotics with low cost hardware

Fontanelli

Palopoli

Rizano

2012

Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2012)

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Abstract-High performance robotics is traditionally considered as an application area reserved to university laboratories and to the research centres of a limited group of company, which can afford high investments in equipments and system engineering. We make the point that this is not necessarily true if an adequate design is used to compensate for the lack of sophisticated sensors. To prove the validity of this idea we propose a concrete case study: driving a car-like vehicle at a high speed with a cost of the hardware below 500 Euros.

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“…Some rely on temporal contrast, implemented through frame differencing [6,5,7], or frame-free light intensity change [3]; others focus on spatial contrast, either using some flavor of pixellevel connectivity [8,9], or by performing spatial-temporal operations [1,10]. In our work, we focus on spatial contrast, an essential cue in the context of image and video analysis.…”

Section: Introductionmentioning

confidence: 99%

Retrieving gray-level information from a Binary Sensor and its application to gesture detection

Gallo¹,

Frosio²,

Gasparini

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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We report on the use of a CMOS Contrast-based Binary Vision Sensor (CBVS), with embedded contrast extraction, for gesture detection applications. The first advantage of using this sensor over commercial imagers is a dynamic range of 120dB, made possible by a pixel design that effectively performs auto-exposure control. Another benefit is that, by only delivering the pixels detecting a contrast, the sensor requires a very limited bandwidth.We leverage the sensor's fast 150µs readout speed, to perform multiple reads during a single exposure; this allows us to estimate gray-level information from the otherwise binary pixels. As a use case for this novel readout strategy, we selected in-car gesture detection, for which we carried out preliminary tests showing encouraging results.

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A 100 $\mu$W 128 $\times$ 64 Pixels Contrast-Based Asynchronous Binary Vision Sensor for Sensor Networks Applications

Cited by 76 publications

References 15 publications

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

High speed robotics with low cost hardware

Retrieving gray-level information from a Binary Sensor and its application to gesture detection

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