Mobile Robot Localization and Map Building: A Multisensor Fusion Approach

By José A. Castellanos, Juan D. Tardós. 2000

311 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cee9c9f2a22161dc01eec774008984314468ef2c

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Citations in the corpus

Active Policy Learning for Robot Planning and Exploration under Uncertainty (2007, methodology): “In the first step, a Bayesian regression model is learned to map the policy parameters to the estimates of the expected cost function obtained from previous simulations.”
Cooperative concurrent mapping and localization (2002, methodology): “Our work uses a feature-based representation [21], [ 22 ], [23].”
Navigation Strategies for Exploring Indoor Environments (2002, background): “SLAM techniques seek to integrate the information collected by the robot during navigation into the most accurate map possible (Castellanos and Tardos 2000).”
Hierarchical SLAM: real-time accurate mapping of large environments (2005, background): “It is well known that, even in small environments, imposing loop consistency increases the precision of the resulting map [13].”
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments (2002, methodology): “More recently, a pair-wise constraint interpretation tree algorithm has been applied to mobile robot localisation [104, 26], which produces equivalent association results to MCS (including the case where a priori pose information is unavailable).”
The SPmap: a probabilistic framework for simultaneous localization and map building (1999, background): “This article proposes a new probabilistic framework adapted to the problem of simultaneous localization and map building: the symmetries and perturbations map (SPmap) [9] which is based on a general representation of uncertain geometric information.”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, methodology): “The first family, for historical reasons often referred to as SLAM algorithms (Castellanos and Tardós 2000; Dissanayake, DurrantWhyte, and Bailey 2000; Leonard and Feder 1999), relies on the extended Kalman filter to estimate joint posteriors over maps and robot poses.”
Robotic mapping: a survey (2003, methodology): “One family of probabilistic approaches employ Kalman filters to estimate the map and the robot location [14, 20, 27, 38, 55, 73, 104].”
Robust Mapping and Localization in Indoor Environments Using Sonar Data (2002, methodology): “Related recent research by ourselves and others that adopts a feature-based representation can be found in Feder, Leonard, and Smith (1999), Castellanos and Tardós (1999), Dissanayake et al. (2001), and Guivant and Nebot (2001).”
Ltd Registered in England and Wales Registered Number : 1072954 Registered office : (2004, background): “They can be used separately (Gutmann and Schlegel 1996) or in combination (Castellanos and Tardos 1999).”
A general framework for multiresolution image fusion: from pixels to regions (2003, methodology): “Here, fusion is commonly used to identify the environment in which the robot or intelligent system evolves [23, 24 ] and for navigation [25,26].”
Data association in stochastic mapping using the joint compatibility test (2001, methodology): “Juan D. Tardós was born in Huesca, Spain, in 1961.”
Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework (2004, methodology): “See Castellanos and Tardós (2000) for more detail.”
Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva (2000, methodology): “One popular family of approaches, known as SLAM algorithms [22, 23, 84, 85, 134], employs Kalman lters [72, 92] for concurrently estimating robot poses and maps.”
A review of mobile robots: Concepts, methods, theoretical framework, and applications (2019, methodology): “In the literature, there are many techniques for map representation.(104) Nevertheless, main real-world characteristics are not well represented in the world maps.”