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Chapter 3: Data Collection and Modeling 37Although a powerful laser was used to counter ambient lighting, we could notcompensate for spectral reflections since the metallic surfaces under a vehicle exhibitstrong spectral reflection properties. A laser further complicates this problem asinternal reflections lead to significant errors in range estimation. A promisingapproach for this problem involves the use of an optical filter tuned to the frequency ofthe powerful laser. The filter allows the data collection to isolate the initial reflectionof the laser and thus improve the range sensing capabilities. The other noise issue thatwe would like to discuss involves the jerks in trajectory of the scanning mechanism.We have assumed a linear and smooth trajectory under the vehicle in the data that wehave presented.Another significant problem in range scanning underneath a vehicle is that of viewocclusions. The obvious occlusion is that the camera can only view one side of acomponent (the bottom side facing straight down towards the ground). The muffler forexample in the Figure 3.7(d) is a one-sided view. Without dismantling the car, rangescanner cannot extract geometry of the other side of the muffler. Such an occlusionshould illustrate the potential of other occlusions such as one object partially coveringanother object from the range sensor. The objects underneath a vehicle have variousshapes and scales located at different depths. For example in Figure 3.7(b), the bentpipe that connects the muffler and the catalytic converter is occluded by the muffler atthe time of scanning. The solution to this problem is to use multiple scans to fill asmuch as possible the areas without information. This solution is a laborious onebecause multiple fields of view imply multiple calibration procedure iterations. Thedifferent views and scanning angles are extremely restricted by the low groundclearance under a vehicle. Thus an integration and fusion of multiple scans onlypartially fills the occlusion holes, but significantly enhances the data. As a result, wescan underneath a vehicle with multiple passes and at different angles. The finalchallenge that we consider with the data collection is the data redundancy inherent tolaser range scanning. A single range image with 512 x 512 pixels yields over 250,000data points. With additional scans to overcome occlusions and to achieve full coverageunder a vehicle, this number quickly grows to several million data points. This largedata set allows high fidelity geometry that other 3D sensors do not offer, but the priceis that of data redundancy and a potential data overload. The data that we present inFigure 3.7(d) is a 40 megabyte VRML model with 10 million vertices and 15 milliontriangles.We have presented the procedure and capability of data collection using a 3D rangescanner in this chapter. We now have real world objects in a format that computerscan attempt to understand. In Chapter 4, we would like to outline our approach toshape description and discuss the building blocks of our algorithm in detail.
Chapter 3: Data Collection and Modeling 38Mosaic of 11 range imagesColor Image(a)(b)(c)Figure 3.7: Data acquisition for under vehicle inspection. (a) A pre-calibratedscanning mechanism in action. (b) The mosaic of range images as the output from thescanner. (c) Close-up color image of the scene. (d) Snapshot of the registered 3Dmodel.(d)
Page 1 and 2: To the Graduate Council:I am submit
Page 3 and 4: Acknowledgementsii“Experience is
Page 5 and 6: Contents1 INTRODUCTION.............
Page 7 and 8: List of TablesviTable 2.1:Qualitati
Page 9 and 10: viiiFigure 5.2: Curvature analysis
Page 11 and 12: Chapter 1: Introduction 21.1 Motiva
Page 13 and 14: Chapter 1: Introduction 41.2 Propos
Page 15 and 16: Chapter 1: Introduction 6We then pe
Page 17 and 18: Chapter 2: Literature Review 8Shape
Page 19 and 20: Chapter 2: Literature Review 102.2.
Page 21 and 22: Chapter 2: Literature Review 12Bimb
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Page 25 and 26: Chapter 2: Literature Review 16foun
Page 27 and 28: Chapter 2: Literature Review 18Kort
Page 29 and 30: Chapter 2: Literature Review 20boun
Page 31 and 32: Chapter 2: Literature Review 22Gots
Page 33 and 34: Chapter 2: Literature Review 24Tabl
Page 35 and 36: Chapter 3: Data Collection and Mode
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Page 49 and 50: Chapter 4: Algorithm Overview 404.1
Page 51 and 52: Chapter 4: Algorithm Overview 42Tri
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Page 55 and 56: Chapter 4: Algorithm Overview 46The
Page 57 and 58: Chapter 4: Algorithm Overview 48of
Page 59 and 60: Chapter 4: Algorithm Overview 50∞
Page 61 and 62: Chapter 4: Algorithm Overview 52The
Page 63 and 64: Chapter 4: Algorithm Overview 54smo
Page 65 and 66: Chapter 4: Algorithm Overview 564.2
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Page 69 and 70: Chapter 5: Analysis and Results 60W
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Page 89 and 90: Chapter 5: Analysis and Results 80(
Page 91 and 92: Chapter 6: Conclusions 82complexity
Page 93 and 94: Bibliography 84BIBLIOGRAPHY
Page 95 and 96: Bibliography 86[Biermann, 2001][Bel
Page 97 and 98:
Bibliography 88[Cyr and Kimia, 2001
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Bibliography 90[Gourley, 1998][Gosh
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Bibliography 92[Joshi and Chang, 19
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Bibliography 94[Meyer, 2002][Morse,
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Bibliography 96[Safar et al., 2000]
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Bibliography 98parts,” IEEE Trans
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Vita 100VITASreenivas Rangan Sukuma
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