A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots

By Sebastian Thrun. Int. J. Robotics Res., 2001

592 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/843a29e397864b4bd148356bb580b62ad1314283

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

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Citations in the corpus

Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004, background): “2003b) and earlier related mapping algorithms (Murphy 2000; Thrun 2001) require time logarithmic in the number of features in the map, but they depend linearly on a particle-filter specific parameter (the number of particles).”
Distributed Multirobot Exploration and Mapping (2005, methodology): “In contrast to [3], [6], [32], and [35], our techniques makes no assumptions about the relative locations of robots.”
An Atlas framework for scalable mapping (2003, methodology): “A variety of approaches have been proposed for representing the uncertainty inherent to sensor data and robot motion, including topological [9], particle filter [16], [12], and feature-based [14] models.”
The SLAM problem: a survey (2008, background): “These problems could be avoided if the data association was known [29], what is the same, if the E-step was simplified or eliminated.”
Model based vehicle detection and tracking for autonomous urban driving (2009, background): “The two most common models are the independent beam model (IB) (Moravec, 1988; Burgard et al, 1996; Fox et al, 1999) and the likelihood field model (LF) (Thrun, 2001).”
Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters (2016, methodology): “Our measurement model is derived from that in [6], and [6]’s measurement model is rooted from the independent beam model [24], [25], [26] and likelihood ﬁeld model [27].”
Robust Mapping and Localization in Indoor Environments Using Sonar Data (2002, methodology): “Alternative approaches include the work of Lu and Milios (1997), Gutmann and Konolige (1999), and Thrun (2001).”
Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework (2004, methodology): “A variety of approaches have been proposed for representing the uncertainty inherent to sensor data and robot motion, including topological (Kuipers 2000), particle filter (Thrun 2001; Montemerlo et al. 2002), and feature-based (Smith, Self, and Cheeseman 1990) models.”
On measuring the accuracy of SLAM algorithms (2009, background): “…often referred to as the simultaneous localization and mapping (SLAM) or concurrent mapping and localization (CML) problem [Smith and Cheeseman, 1986; Dissanayake et al., 2000; Gutmann and Konolige, 1999; Hähnel et al., 2003; Montemerlo et al., 2003; Thrun, 2001; Leonard and Durrant-Whyte, 1991].”