http://dl.gi.de/handle/20.500.12116/11106 2026-07-23T21:48:17Z http://dl.gi.de/handle/20.500.12116/11153 Learning from Nature: Biologically Inspired Robot Navigation and SLAM—A Review Sünderhauf, Niko; Protzel, Peter In 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. 2010-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11152 Lifelong Map Learning for Graph-based SLAM in Static Environments Kretzschmar, Henrik; Grisetti, Giorgio; Stachniss, Cyrill In 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. 2010-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11147 News 2010-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11155 Knowledge Processing for Cognitive Robots Tenorth, Moritz; Jain, Dominik; Beetz, Michael Knowledge processing methods are an important resource for robots that perform challenging tasks in complex, dynamic environments. When applied to robot control, such methods allow to write more general and flexible control programs and enable reasoning about the robot’s observations, the actions involved in a task, action parameters and the reasons why an action was performed. However, the application of knowledge representation and reasoning techniques to autonomous robots creates several hard research challenges. In this article, we discuss some of these challenges and our approaches to solving them. 2010-01-01T00:00:00Z