Simultaneous localization and mapping: A feature-based probabilistic approach

Authors:
Piotr SkrzypczyńSki
Affiliations:
Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965, Poznań, Poland
Venue:
International Journal of Applied Mathematics and Computer Science - Special Section: Robot Control Theory Cezary Zielinski
Year:
2009

Citing 12
Cited 0

Estimating uncertain spatial relationships in robotics

Autonomous robot vehicles
Uncertainty representation for mobile robots: perception, modeling and navigation in unknown environments

Fuzzy Sets and Systems
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach

Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Estimation with Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation
A Set Theoretic Approach to Dynamic Robot Localization and Mapping

Autonomous Robots
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association

Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
MonoSLAM: Real-Time Single Camera SLAM

IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments

IEEE Transactions on Robotics
The Effects of Partial Observability When Building Fully Correlated Maps

IEEE Transactions on Robotics
Large-Scale 6-DOF SLAM With Stereo-in-Hand

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.