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Title: Guaranteed SLAM : an interval approach
Author: Mustafa, Mohamed
ISNI:       0000 0004 6498 5240
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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The mapping problem is a major player in mobile robotics, and it is essential for many real applications such as disaster response or nuclear decommissioning. Generally, the robotic mapping is addressed under the umbrella of simultaneous localization and mapping (SLAM). Several probabilistic techniques were developed in the literature to approach the SLAM problem, and despite the good performance, their convergence proof is only limited to linear Gaussian models. This thesis proposes an interval SLAM (i-SLAM) algorithm as a new approach that addresses the robotic mapping problem in the context of interval methods. The noise of the robot sensor is assumed bounded, and without any prior knowledge of its distribution, we specify soft conditions that guarantee the convergence of robotic mapping for the case of nonlinear models with non-Gaussian noise. A new theory about compact sets is developed in the context of real analysis to conclude such conditions. Then, a case study is presented where the performance of i-SLAM is compared to the probabilistic counterparts in terms of accuracy and efficiency. Moreover, this work presents an application for i-SLAM using an RGB-D sensor that operates in unknown environments. Interval methods and computer vision techniques are employed to extract planar landmarks in the environment. Then, a new hybrid data association approach is developed using a modified version of bag-of-features method to uniquely identify different landmarks across timesteps. Finally, the results obtained using the proposed data association approach are compared to the typical least-squares approaches, thus demonstrating the consistency and accuracy of the proposed approach.
Supervisor: Lennox, Barry Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Interval Methods ; Robotics ; SLAM ; Computer Vision