Theses and Dissertations

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    Bag of words (BoW) generation from given features for optimizing feature matching in V-SLAM application
    (2020) Varshney, Kratika; Mandal, Srimanta
    The project aims at generation of Bag of words from given features for optimizing feature matching in V-SLAM application. Simultaneous localization and mapping (SLAM) is the process where an ego vehicle builds a global map of their current environment and uses this map to navigate or deduce its location at any point in time. Visual SLAM is a specific type of SLAM that performs location and mapping functions by leveraging vision based sensors (like monocular or stereo camera) when neither the environment nor the location of the sensor is known. This report illustrates the use of BoW vocabulary for loop detection. A BoW based vocabulary is generated using feature extractor/descriptor by reading a database of images. Also, the report analyses the effect of varying direct index levels on an independent framework known as DLoopDetector. The vocabulary generation and evaluation frameworks are understood, modified for relevant features and experimented upon wrt various parameter choices involved.
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    SLAM (Self localization and mapping)
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2013) Lodha, Aditya V.; Dubey, Rahul
    A fundamental competence of any mobile robot system is the ability to remain localized while operating in an environment. For unknown/partially known environments there is a need to combine localization with automatic mapping to facilitate the localization process. The process of Simultaneous Localization and Mapping (SLAM) is the topic of this thesis. SLAM is a topic that has been studied for more than two decades using a variety of different methodologies, yet it deployment has been hampered by problems in terms of computational complexity, consistent integration of partially observable features, divergence due to linearization of the process, introduction of topological constraints into the estimation process, and efficient handling of ambiguities in the data-association process. Purpose of this study is to understand the basic problem of SLAM. Using ultrasonic sensor and wheel encoder only, implementation of SLAM is approached. This is a very basic method of SLAM, but it is necessary to understand the problem of SLAM. The method used in this study is very simple to understand and is based on modified SLAM [3].