Large-scale semantic mapping and reasoning with heterogeneous modalities

Pronobis, Andrzej; Jensfelt, Patric

doi:10.1109/icra.2012.6224637

Cited by 221 publications

(201 citation statements)

References 15 publications

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“…This information can be used to improve the object searching task [4,5]. A final application of place categorization constitutes the high level representation of indoor environments by autonomous robots [6,7].…”

Section: Introductionmentioning

confidence: 99%

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

et al. 2013

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This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens.In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality.

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Section: Introductionmentioning

confidence: 99%

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

et al. 2013

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“…Some tried to model the environment through the topology of open space in geometric map, like [6], where they employed a series of kernels for semantic labeling of regions. Some others like [5], [14] and [9] proposed spatial maps, enhanced conceptually by object recognition in regions of the map. [5] proposed a composition of two hierarchical maps, semantic and geometric anchored together.…”

Section: Related Workmentioning

confidence: 99%

“…In [14] a framework of a multilayer conceptual map is developed, representing the spatial and functional properties of the environment. And [9] introduced a comprehensive framework of spatial knowledge representation for large scale semantic mapping.…”

Section: Related Workmentioning

confidence: 99%

Modeling of a Large Structured Environment

Shahbandi

Åstrand

2014

Advances in Autonomous Robotics Systems

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Abstract. AIMS project attempts to link the logistic requirements of an intelligent warehouse and state of the art core technologies of automation, by providing an awareness of the environment to the autonomous systems and vice versa. In this work we investigate a solution for modeling the infrastructure of a structured environment such as warehouses, by the means of a vision sensor. The model is based on the expected pattern of the infrastructure, generated from and matched to the map. Generation of the model is based on a set of tools such as closed-form Hough transform, DBSCAN clustering algorithm, Fourier transform and optimization techniques. The performance evaluation of the proposed method is accompanied with a real world experiment.

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“…The progenitors of the multi-layered conceptual mapping suggested a hierarchical structure that integrates the metric map, navigation map, topological map and conceptual map; and the map building process is to be regarded as a human-like decomposition and categorization of space [15][16][17]. Similarly, Pronobis and Jensfelt introduced a probabilistic framework combining heterogeneous information, uncertainty and human input for semantic mapping [18].…”

Section: Introductionmentioning

confidence: 99%

“…Typical forms of this information include temporal consistency [7,29], spatial consistency [1,18,20,30,31] and place-object relationships [29,32]. Besides the methods that detect change-point directly [31], many systems adopt probabilistic graphical models like the hidden Markov model (HMM) [20,30] or the conditional random field (CRF) [1] to incorporate the spatial dependencies between places.…”

Section: Introductionmentioning

confidence: 99%

Towards generalization of semi-supervised place classification over generalized Voronoi graph

Shi

Kodagoda

2013

Robotics and Autonomous Systems

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With the progress of human-robot interaction (HRI), the ability of a robot to perform highlevel tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels.Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semi-supervised place classification over a generalized voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially observed data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected at international university environments.

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Large-scale semantic mapping and reasoning with heterogeneous modalities

Cited by 221 publications

References 15 publications

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

Modeling of a Large Structured Environment

Towards generalization of semi-supervised place classification over generalized Voronoi graph

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