FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges

By Michael Montemerlo, Sebastian Thrun, Daphne Roller, Ben Wegbreit. International Joint Conference on Artificial Intelligence, 2003

1,234 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b1f254c8f1ff7cd5df84e96681320247c8d51194

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Citations in the corpus

Persistent Navigation and Mapping using a Biologically Inspired SLAM System (2010, methodology): “One of the most successful implementations using particle filters is FastSLAM (Montemerlo et al., 2002, 2003).”
Deliberation for autonomous robots: A survey (2017, background): “Deliberation requires a variety of models of the environment, of the robot’s sensors, actuators, skills, tasks, etc. are estimation, parameter acquisition and they at the level of one function, rarely Modeling is a increases, i.e., when deliberation accommodate to changes in its in robotics is ﬁeld ”
Consistency of the EKF-SLAM Algorithm (2006, methodology): “Previous work on EKF-SLAM consistency [10], [4], [8] shows that eventual inconsistency of the algorithm is inevitable 1 A number of algorithms appearing in the recent literature, such as scan alignment [7] and FastSLAM [13], are built on alternative foundations (e.g., data correlation, particle filt”
Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters (2016, methodology): “Another source of efﬁciency improvement is similar to the efﬁciency improvement achieved when switching from FastSLAM 1.0 to FastSLAM 2.0 [21]; in our tracking method, the motion estimate and pose update of the target vehicle take the latest sensor measurement into account.”
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation (2021, methodology): “Concretely SLAM-net encodes the particle filter based FastSLAM algorithm [44] and learns mapping, transition and observation models.”
Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004, methodology): “We begin our exposition with experiments using a real-world benchmark data set, which has commonly used to evaluate SLAM algorithms (Guivant and Nebot 2001; Montemerlo et al. 2003; Neira, Tardós, and Castellanos 2003).”
Incremental smoothing and mapping (2008, background): “[99, 100, 69, 63], which employs a Rao-Blackwellized particle filter that keeps multiple hypotheses of robot trajectories, each with its own map.”
FastSLAM: a factored solution to the simultaneous localization and mapping problem (2002, background): “0 [43], which attempts to solve this problem.”
Simultaneous Localisation and Mapping ( SLAM ) : Part I The Essential Algorithms (2006, background): “0 in real outdoor environments [33] show that the algorithm is capable of generating an accurate map in practice.”
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words (2008, methodology): “From the same principle, the Rao-Blackwellised particle filter (RBpf) enables loop-closure capabilities in SLAM alg orithms (e.g the FastSLAM [ 14 ] framework).”
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters (2007, methodology): “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].”
Active Policy Learning for Robot Planning and Exploration under Uncertainty (2008, methodology): “Given the transition and observation models presented in this section, one can use any approximate SLAM filtering algorithm, such as EKF-SLAM [30], FAST-SLAM [31] or Marginal-SLAM [32], to compute the filtering distribution p(xt|y1:t,a1:t).”
Robust monocular SLAM in dynamic environments (2013, unknown): “Eade and Drummond [10] present a monocular SLAM system that employs a FastSLAM-type [21] particle filter and top-down search to allow real-time performance while mapping hundreds of landmarks.”
Particle Filter SLAM with High Dimensional Vehicle Model (2009, unknown): “0 makes use of an improved proposal distribution in step 6b [27].”
Indoor Tracking: Theory, Methods, and Technologies (2015, background): “A prominent PF solution is the celebrated FastSLAM [68].”