Artículos de revistas
Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context
Fecha
2018-06Registro en:
Gimenez Romero, Javier Alejandro; Amicarelli, Adriana Natacha; Toibero, Juan Marcos; Di Sciascio, Fernando Agustín; Carelli Albarracin, Ricardo Oscar; Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context; Springer; International Journal of Automation and Computing; 15; 3; 6-2018; 310-324
1476-8186
1751-8520
CONICET Digital
CONICET
Autor
Gimenez Romero, Javier Alejandro
Amicarelli, Adriana Natacha
Toibero, Juan Marcos
Di Sciascio, Fernando Agustín
Carelli Albarracin, Ricardo Oscar
Resumen
This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.