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3D imaging for outdoor workspace monitoring

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Title: 3D imaging for outdoor workspace monitoring
Author: Haghighat-Kashani, Ali
Degree Doctor of Philosophy - PhD
Program Electrical and Computer Engineering
Copyright Date: 2011
Publicly Available in cIRcle 2011-09-28
Abstract: This research investigates the use of laser scanners to provide fast and accurate workspace information on excavators. The thesis first evaluates the use of laser scanners to track a mining shovel's dipper position, in order to extract its arm geometry variables in real-time. The low spatial resolution of laser scanners and the need for accurate initialization challenge the reliability and accuracy of laser-based object tracking methods. The first contribution of this work is in introducing a dipper tracking algorithm that provides an accurate, reliable and real-time performance. To accomplish this task, the proposed algorithm uses the shovel dipper's kinematic constraints and pose history in a Particle Filter to perform an efficient global search in the workspace. The result is then used to initialize an Iterative Closest Point algorithm that uses a geometrical model of the dipper to perform a local search and increase the accuracy of the positioning estimate. In the experiments performed on a mining shovel, the algorithm reliably tracked the dipper in real-time, and obtained a mean dipper positioning error of 6.7cm – 0.3% of the dipper range. In contrast to arm geometry, most workspace information cannot be extracted using a single laser scanner since it cannot scan the entire scene at a real-time frame-rate. In addition to having a real-time frame-rate, 3D images have to be accurate and reliable to be useful for workspace variable extraction. Hence, this work investigates whether and how such images can be provided using a laser scanner and an intensity camera. The slow and sparse results of depth sensors (e.g., laser scanner) make them ineffective when used alone. This work proposes a method for fusing depth sensors with an intensity camera to capture accurate and dense 3D images. The proposed fusion uses a super-resolution algorithm based on bilateral filtering to upsample small depth sensor images, with the help of high-resolution images from an intensity camera. The proposed method obtains more accurate results compared to the literature – an error reduction of up to 6.8x based on the Middlebury benchmark – with sharper and more realistic edge definition.
URI: http://hdl.handle.net/2429/37670
Scholarly Level: Graduate

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