Coarse-to-fine global localization for mobile robots with hybrid maps of objects and spatial layouts

Park, Soonyong; Cheong, Howon; Park, Sung-Kee

doi:10.1109/iros.2009.5354193

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…Coarse-to-fine mechanism, as an excellent strategy, has been applied many systems such as vision based pedestrian localization [9], object detection [10], and scene classifi-978-1-4799-7397-2/14/$31.00 © 2014 IEEE cation [11], et al What's more, Park [12] contributed at presenting a coarse-to-fine global localization method for mobile robots navigation based on hybrid maps of objects and spatial layouts. Different from the methods mentioned above, the localization approach presented in this paper is not just a coarse-to-fine process alone.…”

Section: Introductionmentioning

confidence: 99%

“…The motivation of this paper is to present a closed-loop multi-sensor-based framework for mobile robot localization. Unlike the open-loop coarse-to-fine localization systems [7], [9], [10], [12], we provide a localization state monitoring mechanism to fuse multi-sensor measurement. Experiments are conducted in an office environment, and the results exhibits great improvement on speed and stability of robot localization.…”

Section: Introductionmentioning

confidence: 99%

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A multi-sensor-based mobile robot localization framework

Duan

Tian

2014

2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)

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This paper presents a novel multi-sensor-based robot localization framework inspired by human coarse-to-fine recognition mechanism to realize fast and robust localization in the process of robot navigation. This localization framework consists of two parts: coarse place recognition and accurate location estimation. The coarse place recognition is realized using an onboard camera, whereas an image retrieval system is employed. The coarse localization system utilizes feature matching between the observed image and the re-ranked retrieval images to infer the possible locations of the robot. To obtain the accurate pose of robot, a modified particle filter which is mainly based on the laser radar data is implemented. To integrate the two localization stages, the current states of the particles are monitored. Once the information entropy changed greatly or the number of the effective particles is in a low level, a set of pointed particles is generated based on these estimated locations and then injected into the modified particle filter timely to ensure that enough particles are surviving around the correct pose of robot. Experiments are conducted extensively in an office environment and the results exhibits great improvement on speed and stability of mobile robot localization compared to conventional localization methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-sensor-based mobile robot localization framework

Duan

Tian

2014

2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)

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“…In the past, several research projects have been established to build an accurate and complete metric or topological map of the environment based on the data gathered by a mobile robot [1][2][3][4][5][6]. Differences in these projects are mainly related to the methods used by the robot for symbolizing its perception and understanding its current place.…”

Section: Introductionmentioning

confidence: 99%

“…Objectbased place-recognition systems [2,10,12] characterize a specific place by means of the objects belonging to that place. Localization is then carried out by comparing the objects from the robot's point of view and the stored place database.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised place recognition for assistive mobile robots based on local feature descriptions

Karasfi

Tang

Samsudin

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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Place recognition is an important perceptual robotic problem, especially in the navigation process. Previous place-recognition approaches have been used for solving 'global localization' and 'kidnapped robot' problems. Such approaches are usually performed in a supervised mode. In this paper, a robust appearance-based unsupervised place clustering and recognition algorithm is introduced. This method fuses several image features using speed up robust features (SURF) by agglomerating them into a union form of features inside each place cluster. The number of place clusters can be extracted by investigating the SURF-based scene similarity diagram between adjacent images. During a human-guided learning step, the robot captures visual information acquired by an embedded camera and converts them into topological place clusters. Experimental results show the robustness, accuracy, and efficiency of the method, as well as its ability to create topological place clusters for solving global localization and kidnapped robot problems. The performance of the developed system is remarkable in terms of time, clustering error, and recognition precision.

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