A unified visual graph-based approach to navigation for wheeled mobile robots

Hartmann, Jan; Klüssendorff, Jan Helge; Maehle, Erik

doi:10.1109/iros.2013.6696610

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…With this purpose, we first tried to implement an autonomous navigation system using the well-known ROS navigation stack. Nevertheless, we found that the tools provided by this stack were too much geared towards the use of two-dimensional grid maps, which makes difficult the integration of alternative localization algorithms as can be seen in [36]. For this reason, we designed an alternative framework that-among other interesting features-provides enough flexibility to integrate localization algorithms of different nature in an easy and convenient way.…”

Section: Framework Designmentioning

confidence: 99%

“…This favors modularity and eliminates conversions and other undesired extra processes required to integrate alternative environment representations with the navigation stack. An example of this can be found in [36], where the authors explain the integration of a graph-based visual SLAM system with the navigation stack planning and control modules. The authors report that they had to create a grid map from their native graph representation and that this extra process introduced additional problems that even forced to discard two of the three scenarios for the path-planning experiments carried out for the paper.…”

Section: Framework Designmentioning

confidence: 99%

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Framework for Fast Experimental Testing of Autonomous Navigation Algorithms

Muñoz-Bañón¹,

Pino²,

Candelas³

et al. 2019

Applied Sciences

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Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that allows the generation of new autonomous navigation applications in a fast and simple way. Our framework provides a powerful basic structure based on abstraction levels that ease the implementation of minimal solutions with all the functionalities required to implement a whole autonomous system. This approach helps to keep the focus in any sub-problem of interest (i.g. localization or control) while permitting to carry out experimental tests in the context of a complete application. To show the validity of the proposed framework we implement an autonomous navigation system for a ground robot using a localization module that fuses global navigation satellite system (GNSS) positioning and Monte Carlo localization by means of a Kalman filter. Experimental tests are performed in two different outdoor environments, over more than twenty kilometers. All the developed software is available in a GitHub repository.

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Section: Framework Designmentioning

confidence: 99%

Section: Framework Designmentioning

confidence: 99%

Framework for Fast Experimental Testing of Autonomous Navigation Algorithms

Muñoz-Bañón¹,

Pino²,

Candelas³

et al. 2019

Applied Sciences

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“…Several SLAM implementations utilizing these libraries were presented. [16][17][18] The state-of-theart in SLAM focuses on the optimization of poses and landmarks which typically refer to local image features (e.g. SIFT 19 ), geometric features (e.g.…”

Section: Object-based Slammentioning

confidence: 99%

Online semantic mapping of logistic environments using RGB-D cameras

Himstedt

Maehle

2017

International Journal of Advanced Robotic Systems

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Automated guided vehicles require spatial representations of their working spaces in order to ensure safe navigation and carry out high-level tasks. Typically, these models are given by geometric maps. Even though these enable basic robotic navigation, they off-the-shelf lack the availability of task-dependent information required to provide services. This article presents a semantic mapping approach augmenting existing geometric representations. Our approach demonstrates the automatic annotation of map subspaces on the example of warehouse environments. The proposals of an object recognition system are integrated in a graph-based simultaneous localization and mapping framework and eventually propagated into a global map representation. Our system is experimentally evaluated in a typical warehouse consisting of common object classes expected for this type of environment. We discuss the novel achievements and motivate the contribution of semantic maps toward the operation of automated guided vehicles in the context of Industry 4.0.

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