Automatic mapping of dynamic office environments

Kunz, C.; Willeke, Thomas; Nourbakhsh, Illah

doi:10.1109/robot.1997.614385

Cited by 13 publications

(14 citation statements)

References 18 publications

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“…For automatic mapping in [12] the Baum-Welch algorithm has been used for model learning. In contrast to the above mentioned topological approaches, Kunz et al [14] proposes a topological approach, which heavily relies on odometry in order to handle environment dynamics better. All these approaches are robust, but have the drawback of losing in precision with respect to the fully metric ones: the robot pose is represented by a location without precise metric information.…”

Section: Related Workmentioning

confidence: 99%

Hybrid simultaneous localization and map building: a natural integration of topological and metric

Tomatis

Nourbakhsh

Siegwart

2003

Robotics and Autonomous Systems

141

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In this paper the metric and topological paradigms are integrated in a hybrid system for both localization and map building. A global topological map connects local metric maps, allowing a compact environment model, which does not require global metric consistency and permits both precision and robustness. Furthermore, the approach handles loops in the environment during automatic mapping by means of the information of the multimodal topological localization. The system uses a 360 • laser scanner to extract corners and openings for the topological approach and lines for the metric method. This hybrid approach has been tested in a 50 m × 25 m portion of the institute building with the fully autonomous robot Donald Duck. Experiments are of four types: maps created by a complete exploration of the environment are compared to estimate their quality; test missions are randomly generated in order to evaluate the efficiency of the approach for both the localization and relocation; the fourth type of experiments shows the practicability of the approach for closing the loop.

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Section: Related Workmentioning

confidence: 99%

Hybrid simultaneous localization and map building: a natural integration of topological and metric

Tomatis

Nourbakhsh

Siegwart

2003

Robotics and Autonomous Systems

141

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“…A map is needed to perform the robot localization, but, on the other hand, it is necessary to know the robot's position in order to build the map. This challenge has been often referred to as the SLAM (Simultaneous Localization and Mapping) problem, existing a vast literature about it [18,1,30,9,19,20].…”

Section: Introductionmentioning

confidence: 99%

Geographical information systems for map based navigation in urban environments

Tur

Zinggerling

Murtra

2009

Robotics and Autonomous Systems

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In order to solve most of the existing mobile robotics applications, the robot needs some information about its spatial environment encoded in what it has been commonly called a map. The knowledge contained in such a map, whichever the approach used to obtain it, will mainly be used by the robot to have the ability to navigate in a given environment, that is, to reach any goal from any start point while avoiding static and dynamic obstacles. We are describing in this paper a method that allows a robot or team of robots to navigate in large urban areas for which an existing map in a standard human understandable fashion is available. As detailed maps of most urban areas already exist, it will be assumed that a map of the zone where the robot is supposed to work into is given which has not been constructed using the robot's own sensors. We propose in this paper the use of an existing Geographical Information System based map of an urban zone so that a robot or a team of robots can connect to this map and use it to navigation purposes. Details of the implemented system architecture as well as a position tracking experiment in a real outdoor environment, a University Campus, are provided.

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“…Re-Observation: Let be the new observation in the robot frame. The measurement equation is defined as: (12) is temporarily included in the state to apply the extended Kalman filter. However if prediction satisfies the validation test (13) where , is a number taken from a distribution with degrees of freedom and is the level on which the hypothesis of pairing correctness is rejected, then is a re-observation of .…”

Section: Metric Localization and Map Buildingmentioning

confidence: 99%

“…In [10] the Baum-Welch algorithm is used for model learning. [12] proposes a topological approach, which heavily rely on odometry in order better to handle dynamic environments. While being robust, the drawback of these approaches is the loss of precision: The robot pose is represented by a location without precise metric information.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous localization and map building: a global topological model with local metric maps

Tomatis

Nourbakhsh

Siegwart

Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics I

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In this paper an approach combining the metric and topological paradigm for simultaneous localization and map building is presented The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consistency and permits both precision and robust- ness. The method uses a 360 degree laser scanner in order to extract comers and openings for the topological approach and lines for the metric localization. The approach has been tested in a 30x 25 m portion of the institute building with the fully autonomous robot Donald Duck An experiment consists of a complete exploration and a set of test missions. Three experiments have been performed for a total of 15 test missions, which have been randomly defined and completed with a success ratio of 87%.

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Automatic mapping of dynamic office environments

Cited by 13 publications

References 18 publications

Hybrid simultaneous localization and map building: a natural integration of topological and metric

Hybrid simultaneous localization and map building: a natural integration of topological and metric

Geographical information systems for map based navigation in urban environments

Simultaneous localization and map building: a global topological model with local metric maps

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