To the Graduate Council: I am submitting herewith a thesis written by ...

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.