Millimeter-Precision Laser Rangefinder Using a Low-Cost Photon Counter

In this book we successfully demonstrate a millimeter-precision laser rangefinder using a low-cost photon counter. An application-specific integrated circuit (ASIC) comprises timing circuitry and single-photon avalanche diodes (SPADs) as the photodetectors. For the timing circuitry, a novel binning architecture for sampling the received signal is proposed which mitigates non-idealities that are inherent to a system with SPADs and timing circuitry in one chip

Design and characterization of a CMOS 3-D image sensor based on single photon avalanche diodes

The design and characterization of an imaging system is presented for depth information capture of arbitrary three-dimensional (3-D) objects. The core of the system is an array of 32 × 32 rangefinding pixels that independently measure the time-of-flight of a ray of light as it is reflected back from the objects in a scene. A single cone of pulsed laser light illuminates the scene, thus no complex mechanical scanning or expensive optical equipment are needed. Millimetric depth accuracies can be reached thanks to the rangefinder's optical detectors that enable picosecond time discrimination. The detectors, based on a single photon avalanche diode operating in Geiger mode, utilize avalanche multiplication to enhance light detection. On-pixel high-speed electrical amplification can therefore be eliminated, thus greatly simplifying the array and potentially reducing its power dissipation. Optical power requirements on the light source can also be significantly relaxed, due to the array's sensitivity to single photon events. A number of standard performance measurements, conducted on the imager, are discussed in the paper. The 3-D imaging system was also tested on real 3-D subjects, including human facial models, demonstrating the suitability of the approach

High-frequency scannerless imaging laser radar for industrial inspection and measurement applications

This report describes the development and testing of a high-frequency scannerless imaging laser radar system to evaluate its viability as an industrial inspection and measurement sensor. We modified an existing 5.5-Mhz scannerless laser radar to operate at 150 Mhz, and measured its performance including its spatial resolution and range resolution. We also developed new algorithms that allow rapid data reduction with improved range resolution. The resulting 150-Mhz ladar system demonstrated a range resolution of better than 3 mm, which represents nearly a factor-of-100 improvement in range resolution over the existing scannerless laser radar system. Based on this work, we believe that a scannerless range imager with 1- to 2-mm range resolution is feasible. This work was performed as part of a small-business CRADA between Sandia National Laboratories and Perceptron, Inc

Characteristics of flight simulator visual systems

The physical parameters of the flight simulator visual system that characterize the system and determine its fidelity are identified and defined. The characteristics of visual simulation systems are discussed in terms of the basic categories of spatial, energy, and temporal properties corresponding to the three fundamental quantities of length, mass, and time. Each of these parameters are further addressed in relation to its effect, its appropriate units or descriptors, methods of measurement, and its use or importance to image quality

Sensors for autonomous navigation and hazard avoidance on a planetary micro-rover

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1993.Includes bibliographical references (p. 263-264).by William N. Kaliardos.M.S

Acquisition and modeling of 3D irregular objects.

by Sai-bun Wong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 127-131).Abstract --- p.vAcknowledgment --- p.viiChapter 1 --- Introduction --- p.1-8Chapter 1.1 --- Overview --- p.2Chapter 1.2 --- Survey --- p.4Chapter 1.3 --- Objectives --- p.6Chapter 1.4 --- Thesis Organization --- p.7Chapter 2 --- Range Sensing --- p.9-30Chapter 2.1 --- Alternative Approaches to Range Sensing --- p.9Chapter 2.1.1 --- Size Constancy --- p.9Chapter 2.1.2 --- Defocusing --- p.11Chapter 2.1.3 --- Deconvolution --- p.14Chapter 2.1.4 --- Binolcular Vision --- p.18Chapter 2.1.5 --- Active Triangulation --- p.20Chapter 2.1.6 --- Time-of-Flight --- p.22Chapter 2.2 --- Transmitter and Detector in Active Sensing --- p.26Chapter 2.2.1 --- Acoustics --- p.26Chapter 2.2.2 --- Optics --- p.28Chapter 2.2.3 --- Microwave --- p.29Chapter 2.3 --- Conclusion --- p.29Chapter 3 --- Scanning Mirror --- p.31-47Chapter 3.1 --- Scanning Mechanisms --- p.31Chapter 3.2 --- Advantages of Scanning Mirror --- p.32Chapter 3.3 --- Feedback of Scanning Mirror --- p.33Chapter 3.4 --- Scanning Mirror Controller --- p.35Chapter 3.5 --- Point-to-Point Scanning --- p.39Chapter 3.6 --- Line Scanning --- p.39Chapter 3.7 --- Specifications and Measurements --- p.41Chapter 4 --- The Rangefinder with Reflectance Sensing --- p.48-58Chapter 4.1 --- Ambient Noises --- p.49Chapter 4.2 --- Occlusion/Shadow --- p.49Chapter 4.3 --- Accuracy and Precision --- p.50Chapter 4.4 --- Optics --- p.53Chapter 4.5 --- Range/Reflectance Crosstalk --- p.56Chapter 4.6 --- Summary --- p.58Chapter 5 --- Computer Generation of Range Map --- p.59-75Chapter 5.1 --- Homogenous Transformation --- p.61Chapter 5.2 --- From Global to Viewer Coordinate --- p.63Chapter 5.3 --- Z-buffering --- p.55Chapter 5.4 --- Generation of Range Map --- p.66Chapter 5.5 --- Experimental Results --- p.68Chapter 6 --- Characterization of Range Map --- p.76-90Chapter 6.1 --- Mean and Gaussian Curvature --- p.76Chapter 6.2 --- Methods of Curvature Generation --- p.78Chapter 6.2.1 --- Convolution --- p.78Chapter 6.2.2 --- Local Surface Patching --- p.81Chapter 6.3 --- Feature Extraction --- p.84Chapter 6.4 --- Conclusion --- p.85Chapter 7 --- Merging Multiple Characteristic Views --- p.91-119Chapter 7.1 --- Rigid Body Model --- p.91Chapter 7.2 --- Sub-rigid Body Model --- p.94Chapter 7.3 --- Probabilistic Relaxation Matching --- p.95Chapter 7.4 --- Merging the Sub-rigid Body Model --- p.99Chapter 7.5 --- Illustration --- p.101Chapter 7.6 --- Merging Multiple Characteristic Views --- p.104Chapter 7.7 --- Mislocation of Feature Extraction --- p.105Chapter 7.7.1 --- The Transform Matrix for Perfect Matching --- p.106Chapter 7.7.2 --- Introducing The Errors in Feature Set --- p.108Chapter 7.8 --- Summary --- p.113Chapter 8 --- Conclusion --- p.120-126References --- p.127-131Appendix A - Projection of Object --- p.A1-A2Appendix B - Performance Analysis on Rangefinder System --- p.B1-B16Appendix C - Matching of Two Characteristic views --- p.C1-C

Calibration of scanning laser range cameras with applications for machine vision

Range images differ from conventional reflectance images because they give direct 3-D information about a scene. The last five years have seen a substantial increase in the use of range imaging technology in the areas of robotics, hazardous materials handling, and manufacturing. This has been fostered by a cost reduction of reliable range scanning products, resulting primarily from advanced development of computing resources. In addition, the improved performance of modern range cameras has spurred an interest in new calibrations which take account of their unconventional design. Calibration implies both modeling and a numerical technique for finding parameters within the model. Researchers often refer to spherical coordinates when modeling range cameras. Spherical coordinates, however, only approximate the behavior of the cameras. We seek, therefore, a more analytical approach based on analysis of the internal scanning mechanisms of the cameras. This research demonstrates that the Householder matrix [14] is a better tool for modeling these devices. We develop a general calibration technique which is both accurate and simple to implement. The method proposed here compares target points taken from range images to the known geometry of the target. The calibration is considered complete if the two point sets can be made to match closely in a least squares sense by iteratively modifying model parameters. The literature, fortunately, is replete with numerical algorithms suited to this task. We have selected the simplex algorithm because it is particularly well suited for solving systems with many unknown parameters. In the course of this research, we implement the proposed calibration. We will find that the error in the range image data can be reduced from more that 60 mm per point rms to less than 10 mm per point. We consider this result to be a success because analysis of the results shows the residual error of 10 mm is due solely to random noise in the range values, not from calibration. This implies that accuracy is limited only by the quality of the range measuring device inside the camera