Año: 2018
Autores: Gimenez, J.; Amicarelli, A.; Toibero, M.; di Sciascio, F.; Carelli, R.;
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 im-
proved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with es-
timates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM prob-
lem, 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.
Link: https://link.springer.com/article/10.1007/s11633-017-1109-4