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 ...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.展开更多
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 al...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.展开更多
针对传统的迭代条件模式(iterated conditional model,ICM)算法应用于遥感影像分割时容易出现离散斑块和孤立点的问题,提出了一种基于马尔科夫随机场(Markov random field,MRF)的改进ICM遥感影像分割算法。首先,在获取初始标记之前加入...针对传统的迭代条件模式(iterated conditional model,ICM)算法应用于遥感影像分割时容易出现离散斑块和孤立点的问题,提出了一种基于马尔科夫随机场(Markov random field,MRF)的改进ICM遥感影像分割算法。首先,在获取初始标记之前加入保边去噪效果良好的双边滤波器(bilateral filter,BF),用于遥感影像的预处理;并用多阈值最大类间方差法(Otsu)获取初始标记,以克服传统的初始标记获取算法中K-means聚类算法类别数不确定和算法复杂度不易控制以及错分现象明显等问题;然后,利用MRF描述像元的空间相关性,形成顾及上下文信息的ICM遥感影像分割算法。通过遥感影像数据分割实例验证,所提方法的分割精度优于传统的ICM算法。展开更多
图像分割是一个经典难题,随着影像医学的发展,图像分割在医学应用中具有特殊的重要意义。马尔可夫随机场(Markov Random Field,即MRF)方法是图像分割中一个极为活跃的研究方向。本文介绍了基于马尔可夫随机场模型的一般理论与图像的关...图像分割是一个经典难题,随着影像医学的发展,图像分割在医学应用中具有特殊的重要意义。马尔可夫随机场(Markov Random Field,即MRF)方法是图像分割中一个极为活跃的研究方向。本文介绍了基于马尔可夫随机场模型的一般理论与图像的关系。并对基于MRF的传统条件迭代模式算法(ICM)进行改进,在初始分割后,对图像的像素点分为两类:稳定点和不稳定点,用队列存储不稳定点,每次迭代只对队列里面的不稳定点进行计算,以减少运算量。实验结果表明,改进的算法能够大幅度提高计算效率。展开更多
基金Argentinean National Council for Scientific Research (CONICET)the National University of San Juan (UNSJ) of ArgentinaNVIDIA Corporation for their support
文摘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.
基金supported by the National Council for Scientific and Technological Research(CONICET)the National University of San Juan(UNSJ)
文摘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.
文摘图像分割是一个经典难题,随着影像医学的发展,图像分割在医学应用中具有特殊的重要意义。马尔可夫随机场(Markov Random Field,即MRF)方法是图像分割中一个极为活跃的研究方向。本文介绍了基于马尔可夫随机场模型的一般理论与图像的关系。并对基于MRF的传统条件迭代模式算法(ICM)进行改进,在初始分割后,对图像的像素点分为两类:稳定点和不稳定点,用队列存储不稳定点,每次迭代只对队列里面的不稳定点进行计算,以减少运算量。实验结果表明,改进的算法能够大幅度提高计算效率。
