SLAM Gets a PHD: New Concepts in Map Estimation
MetadataShow full item record
Having been referred to as the Holy Grail of autonomous robotics research, simultaneous localization and mapping (SLAM) lies at the core of most the autonomous robotic applications . This article explains the recent advances in the representations of robotic sensor measurements and the map itself as well as their consequences on the robustness of SLAM. Fundamentally, the concept of a set-based measurement and map state representation allows all of the measurement information, spatial and detection, to be incorporated into joint Bayesian SLAM frameworks. Modeling measurements and the map state as random finite sets (RFSs) rather than the traditionally adopted random vectors is not merely a triviality of notation. It will be demonstrated that a set-based framework circumvents the necessity for any fragile data association and map management heuristics, which are necessary in vectorbased solutions.
Showing items related by title, author, creator and subject.
Veen, Daniel John (2010)Smoothed Particle Hydrodynamics (SPH) is a mesh-free Lagrangian computational method suited to modelling fluids with a freely deforming surface. This thesis describes the development, validation and application of a ...
Adams, M.; Mullane, J.; Vo, Ba-Ngu (2013)In autonomous applications, a vehicle requires reliable estimates of its location and information about the world around it. To capture prior knowledge of the uncertainties in a vehicle's motion response to input commands ...
Mullane, J.; Vo, Ba-Ngu; Adams, M.; Vo, Ba Tuong (2011)This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements ...