A novel soft-computing technique to segment satellite images for mobile robot localization and navigation

Dogruer, C.U.; Koku, Ahmet Buğra; Dölen, Melik

doi:10.1109/iros.2007.4399494

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…As illustrated in Fig. 4, the satellite images were segmented into four elements: buildings (in black color), forest (light gray), and field (dark gray), and road (white) by following the procedure introduced by Dogruer et al 17 During the experiments, the GPS coordinates are obtained from a GPS receiver and the laser range finder measurements are recorded at constant time intervals. Notice that the method suggested for the solution global localization of a mobile robot/platform requires the odometer-and compass (heading) data.…”

Section: Resultsmentioning

confidence: 99%

“…17,[47][48][49][50] Fuzzy clustering, for example, Fuzzy C-Means (FCM) is used to generate the meaningful fuzzy clusters that represents mass number of the pixels with diverse characteristics, these initial clustering serve as a rough guess for the membership's functions in a FIS. Since, FIS cannot be trained, at this stage, in order to estimate the membership function's parameters of the FIS precisely, an ANFIS is constructed/trained by a hybrid learning scheme.…”

Section: Map Creation Using Satellite Imagesmentioning

confidence: 99%

“…For this purpose a hybrid approach employing Fuzzy C-Means (FCM) and Adaptive Network-Fuzzy Inference System (ANFIS) methodology is adapted to segment various elements in such images including buildings, forests, fields, and roads. 17 Then, the Monte Carlo Localization (MCL) technique is used along with these maps to find the pose of a mobile robot which senses around by a laser range finder. 18 In order to successfully apply the proposed method, satellite view of the objects in scene should correlate with the observation of the mobile agent on ground.…”

Section: Introductionmentioning

confidence: 99%

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Outdoor mapping and localization using satellite images

2010

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Recently, satellite images of most urban settings has become available on the internet. In this study, a novel mapping and global localization approach, which uses these images, is proposed for outdoor mobile robots operating in urban environment. The mapping of large-scale outdoor environments is done by employing the satellite images acquired by remote sensing technology, and then a mapbased approach, that is, Monte Carlo localization is used for localization. The novelty of proposed method is that it uses standard equipment present on almost all autonomous robots and satellite images thus it acts as an alternative to GPS data in urban environments. Extensive field tests are presented to demonstrate the effectiveness of proposed approach.

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Section: Resultsmentioning

confidence: 99%

Section: Map Creation Using Satellite Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Outdoor mapping and localization using satellite images

2010

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“…Maps, which were created with a special segmentation technique, have simplified the outdoor localization significantly. Though it is described elsewhere [15], the segmentation scheme is a vital part of the localization approach proposed here.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…2 and the segmented image is also shown in the same figure. Since the approach described here relies on the digital map of the environment, the segmentation of the satellite image, which is further elaborated in [15], is a vital part of the proposed paradigm.…”

Section: Simulationmentioning

confidence: 99%

Global Urban Localization of an Outdoor Mobile Robot with Genetic Algorithms

Dogruer

Koku

Dölen

Springer Tracts in Advanced Robotics

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Summary. The localization of mobile robots has been studied rigorously in the past. However, only a few studies have focused on developing specific Genetic Algorithms (GAs) to address the localization problem effectively. In this study; the global urban localization of an outdoor mobile platform is considered with the utilization of the odometer, the laser-rangeq finder measurements and the digital maps created from the relevant satellite images on the Internet. The localization issue is formulated as a constrained optimization problem. The study proposes a GA-based technique to solve the problem at hand efficiently.

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