FastSLAM: a factored solution to the simultaneous localization and mapping problem

By Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit. AAAI/IAAI, 2002

2,991 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6410aacd3f02f12bf78a8b3e5e664d263cee2183

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Locating in fingerprint space: wireless indoor localization with little human intervention (2012, methodology): “While the robotics and computer vision communities have developed techniques for jointly estimating the locations of a robot and a map of an environment, the nature of wireless signal strength prohibits the use of standard SLAM techniques [30, 19].”
The SLAM problem: a survey (2008, methodology): “landmark position and orientation; therefore, there are no important papers using PF for the whole SLAM framework, but there exist few works that deal with the SLAM problem using a combination of PF with other techniques, for instance, the FastSLAM [24] and the fastSLAM2.”
Robotic mapping: a survey (2003, methodology): “Some techniques, such as the FastSLAM algorithm described in [67], promise a reduction to O(log K) complexity for certain situation, by using non-classical statistical sampling techniques for robot path estimation [29, 71] along with efficient tree representations.”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, background): “0 (Montemerlo et al., 2002) to multiple robots in a featurebased environment.”
Visual SLAM: Why filter? (2012, positive): “Other approaches for SLAM are based on Rao-Blackwellised particle filters (RBPF) [35] such as Sim et al.”
Variational End-to-End Navigation and Localization (2018, methodology): “As opposed to traditional methods for autonomous driving which typically rely on distinct algorithms for localization and mapping [7], [8], [9], planning [10], [11], [12], and control [13], [14], end-to-end algorithms attempt to collapse the problem (directly from raw sensory data to output control ”
Particle Filter SLAM with High Dimensional Vehicle Model (2009, methodology): “The Fastslam algorithm [26] is extended by not only including the map states in the linear part, but also the states corresponding to a linear Gaussian substructure present in the model for the platform.”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “Early approaches to SLAM have addressed the problem using Bayesian formulations using extended Kalman filters (Smith et al. (1987)) or particle filters (Montemerlo et al. (2002)).”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “For ground-vehicles, without the help of any artificial beacons or GPS, ego-motion and scene estimation is well explored, and often relies on the popular 2D SICK laser range-finders and wheel odometry used in a Simultaneous Localization And Mapping (SLAM) Framework, as for instance shown by Montemer”
Model based vehicle detection and tracking for autonomous urban driving (2009, methodology): “Briefly, following the derivation in Montemerlo (2003) , it is straightforward to show that the importance weights wt should be:”
PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, methodology): “Among the most successful proposals we can highlight FastSLAM [21], PTAM [22] and, more recently, ORB-SLAM [12], which relies on a fast and continuous tracking of ORB features [23], and a local bundle adjustment step with the continuous observations of the point features.”
Physically consistent state estimation and system identification for contacts (2015, methodology): “problem is very similar to ours [7] [8].”