A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80×25 m illustrate the appropriateness of the approach.     

移动机器人同步定位与地图构建研究进展

同步定位与地图构建（Simultaneous localization and mapping, SLAM）作为能使移动机器人实现全自主导航的工具近来倍受关注.本文对该领域的最新进展进行综述,特别侧重于一些旨在降低计算复杂度的简化算法的分析上,同时对它们进行分类,并指出其优点和不足.本文首先建立了SLAM问题的一般模型,指出了解决SLAM问题的难点;然后详细分析了基于EKF的一些简化算法和基于其他估计思想的方法;最后,对于多机器人SLAM和主动SLAM等前沿课题进行了讨论,并指出了今后的研究方向.     

移动机器人的概率定位方法研究进展

综述了近几年来流行的移动机器人基于概率定位的各种方法,对它们的性能进行了分析比较,所有这些方法都应用贝叶斯规则作为理论基础.首先,介绍了位置跟踪广泛应用的卡尔曼滤波方法和在全局定位方面取得一定成功的马尔可夫定位方法.然后,介绍了计算效率更高的粒子滤波定位方法,即蒙特卡洛法,以及最近自适应采样的粒子滤波方法,它比简单的粒子滤波效率更高.最后, 对概率定位方法的关键技术进行了分析,并探讨了未来的发展趋势.     

A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.     

Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots

Abstract

Not all line or point features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM.     

An Active System for Three-Dimensional Localization of Mobile Robots

This paper presents an active system, which is composed of a laser range finder and four artificial reflectors, for the three-dimensional (3D) localization of a mobile robot. In this system, it will be proved that the position and the orientation of a mobile robot in a 3D space with respect to a reference frame can be determined provided that the four artificial reflectors are not installed in the same plane. Since the artificial reflectors cannot be treated as points in practice, the proposed localization procedure will be formulated as a nonlinear programming problem to account for actual sizes of the artificial reflectors. To show the validity and feasibility of the proposed method, a series of experiments will be given for illustration.     

移动机器人同时定位与地图创建研究进展

对移动机器人的同时定位与地图创建􀁫(Simultaneous Localization and Mapping)的最新研究进行了综述.指出SLAM 面临的问题,介绍了SLAM的基本实现方法.通过对各种改进的SLAM实现方法的性能对比,详尽地分析了如何降低SLAM的复杂度、提高SLAM的鲁棒性等关键技术问题,同时对多机器人协作的SLAM也进行了论述.探讨了SLAM的研究与发展方向.􀁱     

A Discussion of Simultaneous Localization and Mapping

This paper aims at a discussion of the structure of the SLAM problem. The analysis is not strictly formal but based both on informal studies and mathematical derivation. The first part highlights the structure of uncertainty of an estimated map with the key result being “Certainty of Relations despite Uncertainty of Positions”. A formal proof for approximate sparsity of so-called information matrices occurring in SLAM is sketched. It supports the above mentioned characterization and provides a foundation for algorithms based on sparse information matrices. Further, issues of nonlinearity and the duality between information and covariance matrices are discussed and related to common methods for solving SLAM. Finally, three requirements concerning map quality, storage space and computation time an ideal SLAM solution should have are proposed. The current state of the art is discussed with respect to these requirements including a formal specification of the term “map quality”. This article is based on research conducted during the author's Ph.D. studies at the German Aerospace Center (DLR) in Oberpfaffenhofen. Udo Frese was born in Minden, Germany in 1972. He received the Diploma degree in computer science from the University of Paderborn in 1997. From 1998 to 2003 he was a Ph.D. student at the German Aerospace Center in Oberpfaffenhofen. In 2004 he joined University of Bremen. His research interests are mobile robotic, simultaneous localization and mapping and computer vision.     

机器人自适应主动同时定位与建图新方法

针对多机器人的定位与建图受到即时定位与地图构建(SLAM)研究方法和技术不成熟的制约问题,提出一种基于扩展卡尔曼滤波(EKF)的自适应同时定位与建图方法;首先,基于EKF估计方法,将SLAM中机器人运动方式的选取问题转化为一个多目标最优控制问题,机器人选取最优化目标函数的控制输入,从而以主动的、智能的和自适应的方式探索环境;然后,将上述方法推广到多机器人SLAM中,以实现更为准确、高效和鲁棒的定位与建图;仿真结果表明,该方法大大提高了机器人建图的效率、准确性和鲁棒性;该方法用于机器人主动同时定位和建图是可行的、有效的.     

Map Management for Efficient Simultaneous Localization and Mapping (SLAM)

The solution to the simultaneous localization and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is now well understood. Although a number of SLAM implementations have appeared in the recent literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than a few tens of landmarks. This paper presents the theoretical basis and a practical implementation of a feature selection strategy that significantly reduces the computation requirements for SLAM. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map building process statistically inconsistent. Furthermore, it is shown that the computational cost of the SLAM algorithm can be reduced by judicious selection of landmarks to be preserved in the map.