Incremental topological modeling using sonar gridmap in home environment

Choi, Jinwoo; Choi, Minyong; Chung, Wan Kyun

doi:10.1109/iros.2009.5354247

Cited by 18 publications

(16 citation statements)

References 7 publications

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“…So far, most works in that direction aim at segmentation of structured environments, using spectral clustering [10], trumped Generalized Voronoi Graph (GVG) [28], graph based relaxation [13] or other Bayesian based methods [7]. However, little progress has been made in the segmentation problem for semi-structured environments.…”

Section: B Topological Map For Semi-structured Environmentsmentioning

confidence: 99%

See 1 more Smart Citation

A Markov semi-supervised clustering approach and its application in topological map extraction

Liu

Colas

Pomerleau

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we present a novel semi-supervised clustering approach based on Markov process. It deals with data which include abundant local constraints. We apply the designed model to a topological region extraction problem, where topological segmentation is constructed based on sparse human inputs (potentially provided by human experts). The model considers human indications as seeds for topological regions, i.e. the partially labeled data. It results in a regional topological segmentation of connected free space.

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Section: B Topological Map For Semi-structured Environmentsmentioning

confidence: 99%

“…It is hard for a global clustering algorithm to consider these local constraints. For example, the different sizes of free regions could cause scaling problems [26] for spectral sensitive methods, such as spectral clustering [10].…”

Section: B Topological Map For Semi-structured Environmentsmentioning

confidence: 99%

A Markov semi-supervised clustering approach and its application in topological map extraction

Liu

Colas

Pomerleau

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…In the work of Brunskill et al (Brunskill et al, 2007), the initial set of graph nodes is constructed by random sampling of the free space and edges are created depending on the visibility between the nodes while the method proposed by Choi et al (Choi et al, 2009) is initially driven by a quadtree cell decomposition of free space. To support incremental construction of the topological graph during exploration the former detect discontinuities between the eigenvalues of the similarity matrix, while the latter measure the convexity of the regions.…”

Section: Related Workmentioning

confidence: 99%

Constraint-Free Topological Mapping and Path Planning by Maxima Detection of the Kernel Spatial Clearance Density

Papadakis¹,

Gianni²,

Pizzoli³

et al. 2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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Abstract:Asserting the inherent topology of the environment perceived by a robot is a key prerequisite of high-level decision making. This is achieved through the construction of a concise representation of the environment that endows a robot with the ability to operate in a coarse-to-fine strategy. In this paper, we propose a novel topological segmentation method of generic metric maps operating concurrently as a path-planning algorithm. First, we apply a Gaussian Distance Transform on the map that weighs points belonging to free space according to the proximity of the surrounding free area in a noise resilient mode. We define a region as the set of all the points that locally converge to a common point of maximum space clearance and employ a weighed meanshift gradient ascent onto the kernel space clearance density in order to detect the maxima that characterize the regions. The spatial intra-connectivity of each cluster is ensured by allowing only for linearly unobstructed mean-shifts which in parallel serves as a path-planning algorithm by concatenating the consecutive mean-shift vectors of the convergence paths. Experiments on structured and unstructured environments demonstrate the effectiveness and potential of the proposed approach.

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“…위 방법들과는 달리 비전 센서를 이용한 위치 표식(place labeling) 방법 [14] 이 이 용되었고, 하이브리드 지도의 전역 위상학적 지도 작성을 위한 비전 정보에 따른 노드 구성 방법도 제안되었다 [15] . 제안된 방법은 우리의 기존 방법을 기본으로 개발되었 다 [16,17] . 하지만 기존 방법은 격자 지도에 셀 분할 기법 는 기존의 방법이 이용되었다 [18] .…”

Section: Friedman은 레이저 센서를 이용해 작성된 격자지도에unclassified

Topological Modeling using Sonar Grid Map

Choi¹,

Choi²,

Chung³

2011

The Journal of Korea Robotics Society

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This paper presents a method of topological modeling using only low-cost sonar sensors. The proposed method constructs a topological model by extracting sub-regions from the local grid map. The extracted sub-regions are considered as nodes in the topological model, and the corresponding edges are generated according to the connectivity between two sub-regions. A grid confidence for each occupied grid is evaluated to obtain reliable regions in the local grid map by filtering out noisy data. Moreover, a convexity measure is used to extract sub-regions automatically. Through these processes, the topological model is constructed without predefining the number of sub-regions in advance and the proposed method guarantees the convexity of extracted sub-regions. Unlike previous topological modeling methods which are appropriate to the corridor-like environment, the proposed method can give a reliable topological modeling in a home environment even under the noisy sonar data. The performance of the proposed method is verified by experimental results in a real home environment.

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Incremental topological modeling using sonar gridmap in home environment

Cited by 18 publications

References 7 publications

A Markov semi-supervised clustering approach and its application in topological map extraction

A Markov semi-supervised clustering approach and its application in topological map extraction

Constraint-Free Topological Mapping and Path Planning by Maxima Detection of the Kernel Spatial Clearance Density

Topological Modeling using Sonar Grid Map

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