Linthotage Dushantha Lochana Perera

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—It is well accepted that the data association or the correspondence problem is one of the toughest problems faced by any state estimation algorithm. Particularly in robotics, it is not very well addressed. This paper introduces a multidimen-sional assignment (MDA)-based data association algorithm for the simultaneous localization and map building (SLAM)(More)
theory of nonlinear observability is an important tool available for the assessment of highly nonlinear estimation problems such as Simultaneous Localization and Mapping (SLAM). It is shown that all the estimated landmarks must be observed and at least two a priori known landmarks be observed for the nonlinear observability of single vehicle SLAM when(More)
—Data association or the correspondence problem is often considered as one of the key challenges in every state estimation algorithm in robotics. This paper introduces an efficient multi-dimensional assignment based data association algorithm for simultaneous localization and map building (SLAM) problem in mobile robot navigation. Data association in SLAM(More)
— Correct data association is critical for the success of feature based simultaneous localization and mapping (SLAM) of autonomous vehicles or mobile robots. Incorrect associations result in map inconsistency and inaccurate path estimates. Numerous data association techniques proposed in the literature for SLAM assumes a static environment. Ignoring the(More)