Auflistung nach Schlagwort "SLAM"
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- ZeitschriftenartikelA SLAM Overview from a User’s Perspective(KI - Künstliche Intelligenz: Vol. 24, No. 3, 2010) Frese, Udo; Wagner, René; Röfer, ThomasThis paper gives a brief overview on the Simultaneous Localization and Mapping (SLAM) problem from the perspective of using SLAM for an application as opposed to the common view in SLAM research papers that focus on investigating SLAM itself.We discuss different ways of using SLAM with increasing difficulty: for creating a map prior to operation, as a black-box localization system, and for providing a growing online map during operation.We also discuss the common variants of SLAM based on 2-D evidence grids, 2-D pose graphs, 2-D features, 3-D visual features, and 3-D pose graphs together with their pros and cons for applications. We point to implementations available on the Internet and give advice on which approach suits which application from our experience.
- ZeitschriftenartikelLearning from Nature: Biologically Inspired Robot Navigation and SLAM—A Review(KI - Künstliche Intelligenz: Vol. 24, No. 3, 2010) Sünderhauf, Niko; Protzel, PeterIn this paper we summarize the most important neuronal fundamentals of navigation in rodents, primates and humans. We review a number of brain cells that are involved in spatial navigation and their properties. Furthermore, we review RatSLAM, a working SLAM system that is partially inspired by neuronal mechanisms underlying mammalian spatial navigation.
- ZeitschriftenartikelLifelong Map Learning for Graph-based SLAM in Static Environments(KI - Künstliche Intelligenz: Vol. 24, No. 3, 2010) Kretzschmar, Henrik; Grisetti, Giorgio; Stachniss, CyrillIn this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in this paper illustrate the properties of our method using real robots.