Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2010
Öğrenci: AYHAN ÖZGÜR
Danışman: AFŞAR SARANLI
Özet:The main focus of the study is the implementation of a practical indoor localization and mapping algorithm for large scale, structured indoor environments. Building an incremental consistent map while also using it for localization is partially unsolved problem and of prime importance for mobile robot navigation. Within this framework, a combined method consisting of feature based scan matching and FastSLAM algorithm using LADAR and odometer sensor is presented. In this method, an improved data association and localization accuracy is achieved by feeding the SLAM module with better incremental pose information from scan matching instead of raw odometer output. This thesis presents the following contributions for indoor localization and mapping. Firstly a method combining feature based scan matching and FastSLAM is achieved. Secondly, improved geometrical relations are used for scan matching and also a novel method based on vector transformation is used for the calculation of pose difference. These are carefully studied and tuned based on localization and mapping performance failures encountered in different realistic LADAR datasets. Thirdly, in addition to position, orientation information usage in line segment and corner oriented data association is presented as an extension in FastSLAM module. v The method is tested with LADAR and odometer data taken from real robot platforms operated in different indoor environments. In addition to using datasets from the literature, own datasets are collected on Pioneer 3AT experimental robot platform. As a result, a real time working localization algorithm which is pretty successive in large scale, structured environments is achieved.