220,591 research outputs found

    A low-cost ultrasonic 3D measurement device for calibration of Cartesian and non-Cartesian machines

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    The major obstacles to the widespread adoption of 3D measurement systems are accuracy, speed of process and the cost. At present, high accuracy for measuring 3D position has been achieved, and there have been real advances in reducing measurement time, but the cost of such systems remains high. A high-accuracy and high-resolution ultrasonic distance measurement system has been achieved in this project by creating multi-frequency continuous wave frequency modulation (MFCWFM) system. The low-cost system measures dynamic distance (displacements of an ultrasound transmitter) and fixed distance (distances between receivers). The instantaneous distance between the transmitter and each receiver can be precisely determined. New geometric algorithms for transmitter 3D position and receiver positing have also been developed in the current research to improve the measurement system‟s practicability. These algorithms allow the ultrasound receivers to be arbitrarily placed and located by self-calibration following a simple procedure. After the development and testing of the new 3D measurement system, further studies have also been carried out on the system, considering the two major external disturbances: air temperature drifting and ultrasound echo interference. Novel methods have been successfully developed and tested to minimize measurement errors and evaluation of speed of sound. All the enabling research described in the thesis means that it is now possible to build and implement a measurement system at reasonable cost for industrial exploitation. This will have the necessary performance to provide ultrasonic 3D position measurements in real time for monitoring position.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Frequency-modulated continuous-wave LiDAR compressive depth-mapping

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    We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. % but can operate with only one at the cost of doubling the number of measurments. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. %Moreover, we show how a single total-variation minimization and two fast least-squares minimizations, instead of a single complex nonlinear minimization, can efficiently recover high-resolution depth-maps with minimal computational overhead. Moreover, by efficiently storing only 2m2m data points from m<nm<n measurements of an nn pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods

    Dynamic 3D shape measurement based on the phase-shifting moir\'e algorithm

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    In order to increase the efficiency of phase retrieval,Wang proposed a high-speed moire phase retrieval method.But it is used only to measure the tiny object. In view of the limitation of Wang method,we proposed a dynamic three-dimensional (3D) measurement based on the phase-shifting moire algorithm.First, four sinusoidal fringe patterns with a pi/2 phase-shift are projected on the reference plane and acquired four deformed fringe patterns of the reference plane in advance. Then only single-shot deformed fringe pattern of the tested object is captured in measurement process.Four moire fringe patterns can be obtained by numerical multiplication between the the AC component of the object pattern and the AC components of the reference patterns respectively. The four low-frequency components corresponding to the moire fringe patterns are calculated by the complex encoding FT (Fourier transform) ,spectrum filtering and inverse FT.Thus the wrapped phase of the object can be determined in the tangent form from the four phase-shifting moire fringe patterns using the four-step phase shifting algorithm.The continuous phase distribution can be obtained by the conventional unwrapping algorithm. Finally, experiments were conducted to prove the validity and feasibility of the proposed method. The results are analyzed and compared with those of Wang method, demonstrating that our method not only can expand the measurement scope, but also can improve accuracy.Comment: 14 pages,5 figures. ams.or

    The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM

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    New vision sensors, such as the Dynamic and Active-pixel Vision sensor (DAVIS), incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array. These sensors have great potential for high-speed robotics and computer vision because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and very high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called "events") and synchronous grayscale frames. For this purpose, we present and release a collection of datasets captured with a DAVIS in a variety of synthetic and real environments, which we hope will motivate research on new algorithms for high-speed and high-dynamic-range robotics and computer-vision applications. In addition to global-shutter intensity images and asynchronous events, we provide inertial measurements and ground-truth camera poses from a motion-capture system. The latter allows comparing the pose accuracy of ego-motion estimation algorithms quantitatively. All the data are released both as standard text files and binary files (i.e., rosbag). This paper provides an overview of the available data and describes a simulator that we release open-source to create synthetic event-camera data.Comment: 7 pages, 4 figures, 3 table

    Low-Cost Compressive Sensing for Color Video and Depth

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    A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover {multiple} color frames for each of the original measured frames, and each of the recovered frames can be focused at a different depth. The recovery of multiple frames for each measured frame is made possible via high-speed coding, manifested via translation of a single coded aperture; the inexpensive translation is constituted by mounting the binary code on a piezoelectric device. To simultaneously recover depth information, a {liquid} lens is modulated at high speed, via a variable voltage. Consequently, during the aforementioned coding process, the liquid lens allows the camera to sweep the focus through multiple depths. In addition to designing and implementing the camera, fast recovery is achieved by an anytime algorithm exploiting the group-sparsity of wavelet/DCT coefficients.Comment: 8 pages, CVPR 201
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