An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements

By Dirk Hähnel, Wolfram Burgard, Dieter Fox, Sebastian Thrun. IEEE/RJS International Conference on Intelligent RObots and Systems, 2003

672 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/abb27c652df88e55ea5cd5363e288d55038ff868

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In Aerospace Engineering

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, positive): “In [55], FastSLAM was applied to laser data where the matching gives an odometry measure which is then used to weight particles in the resampling phase.”
Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling (2005, methodology): “[8], which uses a fixed proposal distribution obtained from the residual error of the scan-matching process, in principle needs a conservative proposal distribution to cover the worst case and therefore typically needs more particles than our algorithm.”
Mapping and localization with RFID technology (2004, methodology): “Given this map and the maximum likelihood path of the robot computed by the FastSLAM algorithm we can now estimate the locations of the RFID tags.”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “View-based SLAM differs from feature-based SLAM in that no features are explicitly extracted (Hahnel et al., 2003a).”
Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004, methodology): “…al. / Simultaneous Localization and Mapping 707 In our simulations, we focused particularly on the " loop closing " problem, which is generally acknowledged to be one of the hardest problems in SLAM (Lu and Milios 1997; Gutmann and Konolige 1999; Thrun 2000; Bosse et al. 2003; Hähnel et al. 2003a).”
Semantic mapping for mobile robotics tasks: A survey (2015, background): “The representative works described in [14], [17], [18] and [19] prove the necessity for an accurate representation of the robot's surroundings as well as the development of efficient mapping methods.”
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters (2007, background): “In the literature, the mobile robot mapping problem is often referred to as the simultaneous localization and mapping (SLAM) problem[4, 6, 9, 15, 16, 26, 29, 32, 39].”
Distributed Multirobot Exploration and Mapping (2005, methodology): “Similar to the application of Rao-Blackwellised particle filters for mobile robot mapping [23], [13], [5], one can consider a particle filter as recursively computing posterior probability distributions over robot trajectories.”
A Tutorial on Graph-Based SLAM (2010, methodology): “Popular techniques like Kalman and information filters [28], [3], particle filters [22], [12], [9], or information filters [7], [31] fall into this category.”
On behalf of: Multimedia Archives (2012, positive): “One may argue that in these ambiguous cases, particlefilter-based SLAM (Murphy, 1999; Doucet et al., 2000; Hahnel et al., 2003; Montemerlo and Thrun, 2003; Grisetti et al., 2007b) is the ideal solution as it theoretically can handle arbitrarily shaped distributions.”
Using laser range data for 3D SLAM in outdoor environments (2006, background): “Some successful implementations working in corridors and offices include those in [1] and [2].”
Simultaneous localization and mapping (SLAM): part II (2006, methodology): “Several recent FastSLAM hybrids use pose-aligned scans or grids in place of a landmark map [14], [20], [ 24 ].”
Trajectory Optimization using Reinforcement Learning for Map Exploration (2008, background): “More recent mapping research has focused on building maps with robots that do not have access to global position information (Smith et al. 1990 Lu and Milios 1997 Hähnel et al. 2003 Dellaert and Kaess 2006 Eustice et al. 2005 Thrun et al. 2005 Olson et al. 2006).”
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters (2005, background): “As can be seen from this experiment, as soon as the robot gets too uncertain about its pose, it performs place re-visiting actions or chooses exploration actions which also reduce its pose uncertainty due to the information gathered along the path.”