This paper describes a simultaneous planning localization and mapping (SPLAM) methodology focussed on the global localization problem, where the robot explores the environment efficiently and also considers the requisites of the simultaneous localization and mapping algorithm. The method is based on the randomized incremental generation of a data structure called Sensor-based Random Tree, which represents a roadmap of the explored area with an associated safe region. A continuous localization procedure based on B-Splines features of the safe region is integrated in the scheme.
Workload Partitioning of a Bio-inspired Simultaneous Localization and Mapping Algorithm on an Embedded Architecture
