Auflistung nach Autor:in "Stachniss, Cyrill"
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- 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.
- KonferenzbeitragRobotics: Probabilistic methods for state estimation and control(INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt, 2013) Burgard, Wolfram; Stachniss, CyrillProbabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. Probabilistic approaches have been discovered as one of the most powerful ways to address highly relevant problems in mobile robotics, including robot state estimation and localization. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. Robots are inherently uncertain about the state of their environments. Uncertainty arises from sensor limitations, noise, and the fact that most interesting environments are - to a certain degree - unpredictable. When “guessing” a quantity from sensor data, the probabilistic approach computes a probability distribution over what might be the case in the world, instead of generating a single “best guess” only. As a result, a robot using probabilistic methods can gracefully recover from errors, handle ambiguities, and integrate sensor data in a consistent way.