Robotic mapping: a survey

By Sebastian Thrun. 2003

1,725 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cbe046f24f31aace8b61b36e41392a93225029e0

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Citations in the corpus

Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS (2006, background): “This problem is known as simultaneous localization and mapping (SLAM) and there is a vast amount of literature on this topic (see e.g., [21] for a comprehensive survey).”
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing (2006, background): “In addition, in many applications the map of the environment itself is the artifact of interest, e.g., in urban reconstruction, search-and-rescue operations, battlefield reconnaissance etc.”
Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization (2018, background): “Much work has focused on obtaining a globally-consistent map [2], where an accurate absolute localization of a system is presumed.”
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera (2011, background): “There is also extensive literature within the AR and robotics community on Simultaneous Localization and Mapping (SLAM), aimed at tracking a user or robot while creating a map of the surrounding physical environment (see [25]).”
Factor Graphs for Robot Perception (2017, background): “A general overview of the area of SLAM can be found in [19, 59, 6, 187, 186].”
Globally consistent 3D mapping with scan matching (2008, unknown): “Thrun reviews in [27] existing techniques, i.”
Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System (2008, methodology): “This paper was recommended for publication by Associate Editor A. Davison and Editor L. Parker upon evaluation of the reviewers’ comments.”
Distributed Multirobot Exploration and Mapping (2005, methodology): “It is this connection between robot position and map uncertainty that makes the SLAM (simultaneous localization and mapping) problem computationally demanding [4], [33].”
Fitness functions in evolutionary robotics: A survey and analysis (2009, unknown): “In the broader field of autonomous robotics, control learning may focus on selected portions of a robot’s control abilities, such as object recognition [94], path planning and localization [92,93], or error and fault accommodation.”
Real-time correlative scan matching (2009, methodology): “In a Simultaneous Localization and Mapping (SLAM) context [17], the model m is derived from previous lidar observations.”
Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004, background): “(8) The second step of this derivation exploited common (and obvious) independences in SLAM problems (Thrun 2002 ).”
Visual simultaneous localization and mapping: a survey (2012, methodology): “…such as: Atlas Framework (Bosse et al. 2003), Compressed Extended Kalman Filter (CEKF) (Guivant 2002), Sparse Extended Information Filter (SEIF) (Thrun et al. 2002), Divide and Conquer in O ( n ) given by Paz et al. (2008) or Conditionally Independent Submaps (CI-Submaps) developed by Piniés…”
6D SLAM—3D mapping outdoor environments (2007, methodology): “Compared with related approaches from the literature [4], [7], [20]–[23] we do not use a feature representation of the environment.”
Geometry-Aware Learning of Maps for Camera Localization (2017, methodology): “For map optimization, since Lu and Milios [37] first introduced a graph-based method to refine a map with global optimization of local nodes (measurements from odometry), various types of these local-to-global pose graph optimization methods have been proposed [15, 24, 52, 53].”
The evolution of robotics research (2007, background): “A complete survey of mapping methods can be found in [43].”
iSAM: Incremental Smoothing and Mapping (2008, background): “A general overview of the area of SLAM can be found in [3], [10], [25],”