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A new method for the nonlinear transformation of means and covariances in filters and estimators
A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.
Simultaneous localization and mapping: part I
This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the
A solution to the simultaneous localization and map building (SLAM) problem
The paper proves that a solution to the SLAM problem is indeed possible and discusses a number of key issues raised by the solution including suboptimal map-building algorithms and map management.
Simultaneous localization and mapping (SLAM): part II
This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations
Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms
This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM), the probabilistic form of the SLAM problem, essential solution methods and significant implementations, and recent advances in computational methods.
Mobile robot localization by tracking geometric beacons
An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed.
Simultaneous Localization and Mapping with Sparse Extended Information Filters
It is shown that when represented in the information form, map posteriors are dominated by a small number of links that tie together nearby features in the map, which is developed into a sparse variant of the EIF, called the sparse extended information filter (SEIF).
Simultaneous map building and localization for an autonomous mobile robot
Discusses a significant open problem in mobile robotics: simultaneous map building and localization, which the authors define as long-term globally referenced position estimation without a priori
Simultaneous Localization, Mapping and Moving Object Tracking
Based on the SLAM with DATMO framework, practical algorithms are proposed which deal with issues of perception modeling, data association, and moving object detection.
Directed Sonar Sensing for Mobile Robot Navigation
This paper presents a Sonar Sensor Model for Directed Sensing Strategies, which combines model-Based Localization, Simultaneous Map Building, and Simultaneously Map Building and Localization.