Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms

By H. F. Durrant-Whyte, Tim Bailey. 2006

727 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/666b8959abc3be4d6026f2053711a62119bec4a5

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Citations in the corpus

Factor Graphs and GTSAM: A Hands-on Introduction (2012, background): “The goal of SLAM is to simultaneously localize a robot and map the environment given incoming sensor measurements (Durrant-Whyte and Bailey, 2006).”
Visual simultaneous localization and mapping: a survey (2012, methodology): “Previously,DurrantandBaileypresentedatuto-rial divided into two parts that summarizes the SLAM problem (Durrant and Bailey 2006; Bailey and Durrant 2006).”
A Consistent Metric for Performance Evaluation of Multi-Object Filters (2008, background): “Today, multi-object systems research is an established discipline [2]–[4] with a host of diverse application areas including computer vision [5], oceanography [6], [7], autonomous vehicle/robotics [8], remote sensing [9], and biomedical research [10].”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, unknown): “A thorough historical review of the ﬁrst 20 years of the SLAM problem is discussed by Durrant-Whyte and Bailey in [13, 86].”
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery (2013, background): “A comprehensive review can be found in (Durrant-Whyte and Bailey, 2006; Bailey and Durrant-Whyte, 2006).”
Learning visual representations for perception-action systems (2011, background): “In navigation for example, one might caricature current thinking by citing SLAM (Thrun et al. 2005; Durrant-Whyte and Bailey 2006) as a deliberative approach, and placing reactive navigation at the opposite, reactive end of the spectrum (Braitenberg 1984).”
State Estimation for Robotics (2017, background): “Smith et al. (1990) is the classic reference on SLAM and Durrant-Whyte and Bailey (2006); Bailey and Durrant-Whyte (2006) provide a detailed survey of the area.”
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM (2018, background): “(sparse SLAM [4]) or an alternating optimisation (dense SLAM [5]) of map and pose relationship to solve SLAM.”
Indoor Tracking: Theory, Methods, and Technologies (2015, background): “The locations of the anchor modes, however, may not always be known, as in SLAM [5].”
Incremental smoothing and mapping (2008, background): “A general overview of the area of SLAM can be found in [36, 2, 131, 130].”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “∗COBRA Group at the University of New Brunswick, Fredericton, Canada, http://www.ece.unb.ca/COBRA/ Various solutions have been proposed for SLAM (Whyte & Bailey, 2006).”
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words (2008, unknown): “due to inaccurate resampling policies [3].”
Factor Graphs for Robot Perception (2017, methodology): “A general overview of the area of SLAM can be found in [19, 59, 6, 187, 186].”
Data fusion in intelligent transportation systems: Progress and challenges - A survey (2011, methodology): “Simultaneous localization and mapping is a technique used to obtain the static map of the environment and its position in the map [53].”
SKYLINE2GPS: Localization in urban canyons using omni-skylines (2010, background): “In robotics and vision community, several promising simultaneous localization and mapping (SLAM) algorithms have been developed in the last three decades and detailed surveys are available [8].”
AUV Navigation and Localization: A Review (2014, methodology): “It is applicable to both view-based SLAM [37] and feature-based SLAM [38].”
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM (2018, background): “The validation of the two-frame reconstruction performance is of high importance as it is directly connected to the initialisation procedure of the full SLAM system.”
iSAM: Incremental Smoothing and Mapping (2008, background): “A general overview of the area of SLAM can be found in [3], [10], [25],”