2024-03-19T13:25:22Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/4060852020-05-08T14:30:57Zcom_10803_183col_10803_195
nam a 5i 4500
3D pose estimation in complex environments
[Barcelona] :
Universitat Politècnica de Catalunya,
2017
Accés lliure
http://hdl.handle.net/10803/406085
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Peñate Sánchez, Adrián,
autor
1 recurs en línia (102 pàgines)
Tesi
Doctorat
Universitat Politècnica de Catalunya. Institut d'Organització i Control de Sistemes Industrials
2017
Universitat Politècnica de Catalunya. Institut d'Organització i Control de Sistemes Industrials
Tesis i dissertacions electròniques
Andrade-Cetto, Juan,
supervisor acadèmic
Moreno Noguer, Francesc,
supervisor acadèmic
TDX
Although there has been remarkable progress in the pose estimation literature, there are still a number of limitations when existing algorithms must be applied in everyday applications, especially in uncontrolled environments. This thesis has addressed some of these limitations, computing the pose for uncalibrated cameras, computing the pose without knowing the correspondence between 20 and 30 points, computing the pose when the points of interest are unreliable and computing the pose using only depth data.
The problems addressed, and consequently their contributions, have been analyzed in order of increasing complexity. At each new stage of the doctoral thesis existing restrictions for obtaining 30 camera pose increased. The thesis has consisted of four parts on which we will define the contributions made to the field of Computer Vision.
The first contribution of the doctoral thesis has focused on providing a technique for obtaining the pose of an uncalibrated camera more robust and accurate than existing approaches. By the re-formulation of the equations used in calibrated perspectives methods and by studying numerical stability we obtained an extended equation formulation that offered a closed solution to the problem with increased stability in the presence of noise compared to the state of the art.
The second contribution of the thesis has focused on the fact that most algorithms are based on having a set of 20-30 correspondences. This task usually involves the extraction and matching of points of interest. In this thesis it we have developed an algorithm that solves the estimation of correspondences between points and estimate the pose of the camera together, all this in an uncalibrated context. By solving both problems together you can optimize the steps we take much better than by just solving them separately. In articles published as a result of this work we have shown the advantages inherent in this approach.
The third contribution of the thesis has been to provide a solution for estimating the pose of the camera in extreme situations where the image quality is very deteriorated. This is possible through the use of learning techniques from high-quality data and 30 models of the environment and the objects. This approach is based on the notion that by learning from high-quality data we can obtain detectors that are able to recognize objects in the worst circumstances because they know in depth what defines the object in question.
The fourth contribution of the thesis is the creation of a pose estimation method that does not require color information, only depth. By defining local volumetric dense appearance and performing a dense feature extraction over the depth image. Once the dense feature sampling is obtained we perform an energy minimisation taking into account the pairwise terms between individual features. We obtain accuracy comparable to state of the art methods while performing atan arder of magnitude less time per image.
The sum of the above contributions in 30 pose estimation have improved 30 reconstruction tools such as robotic vision and relocation in 30 maps. All contributions have been published in international journals and conferences of reputed scientific prestige in the area.
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