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 multidimensional assignment (MDA)-based data association algorithm for the simultaneous localization and map building (SLAM)(More)
The 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 work 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)
Autonomous navigation in complex unstructured environments has recently stimulated considerable interest among the robotics research community. This paper discusses the major challenges such as robust feature extraction and data association or the correspondence problem faced in achieving the above goal. The interrelationship between the feature extraction(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)
This paper investigates the Centralized Multi-vehicle Simultaneous Localization and Mapping (CMSLAM) problem in the context of the nonlinear observability. Theory is first developed for the nonlinear observability of CMSLAM using the relatively simple unicycle vehicle model, which gives rise to a CMSLAM problem in control affine form. Conditions required(More)
The theory of stochastic observability is vital in describing the performance of Simultaneous Localization and Mapping (SLAM) as a nonlinear stochastic state estimation problem quantifying effects of random noise on its observability. We show that the eigen space corresponding to the stochastically unobservable states of the state error covariance matrix of(More)
Unmodeled systematic and nonsystematic errors in robot kinematics and measurement processes often cause adverse effects in several autonomous navigation tasks. In particular, accumulated sensor biases can render simultaneous localization and mapping (SLAM) algorithms of autonomous vehicles to perform very poorly especially in large unexplored terrains(More)