Simultaneous localization and mapping: A feature-based probabilistic approach

Simultaneous localization and mapping: A feature-based probabilistic approach

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 an...

Vision Based Simultaneous Localization and Mapping

This paper gives an introduction to the Simultaneous Localization and Mapping (SLAM) methods and recent advances in computational methods of SLAM problem for large scale & complex environment. SLAM addresses the problem of a robot navigating through an unknown environment. It is a process by which a mobile robot can build a map of its near-by environment and at the same time use this map to com...

A simultaneous planning localization and mapping approach for robust navigation

This paper presents an algorithm for solving the simultaneous planning localization and mapping (PSLAM) problem, an important key issue for robust navigation in unknown environments. This proposal is a method for integrated exploration, where mobile robot incrementally build a map of this environment while simultaneously use this map to compute the absolute robot localization, and make local de...

A Component-Oriented Approach to Simultaneous Localization and Mapping

The simultaneous localization and mapping (SLAM) problem is central to many mobile robots. The construction of a map of the local environment and the localization of the robot in that map must be accomplished in an incremental manner, even in the presence of significant error and uncertainty in sensor data. Further, the cyclic data dependency presents a challenge that does not lend itself to ro...

Simultaneous Localization and Mapping