Continuous Probabilistic SLAM Solved via Iterated Conditional Modes  

作　　者：J.Gimenez A.Amicarelli J.M.Toibero F.di Sciascio R.Carelli 

机构地区：[1]Automatics Institute(INAUT), National University of San Juan-CONICET

出　　处：《International Journal of Automation and computing》2019年第6期838-850,共13页国际自动化与计算杂志（英文版）

基　　金：Argentinean National Council for Scientific Research (CONICET);the National University of San Juan (UNSJ) of Argentina;NVIDIA Corporation for their support

摘　　要：This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field(MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes(ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field(MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes(ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.

关 键 词：PROBABILISTIC simultaneous localization and mapping(SLAM) dynamic obstacles Markov random fields(MRF) ITERATED CONDITIONAL modes(ICM) kernel estimator 

分 类 号：TP[自动化与计算机技术]