Automatic semantic maps generation from lexical annotations

Rangel, José Carlos; Cazorla, Miguel; García-Varea, Ismael; Romero-González, Cristina; Martínez‐Gómez, Jesús

doi:10.1007/s10514-018-9723-8

Cited by 13 publications

(11 citation statements)

References 38 publications

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“…Some studies on semantic mapping using two-dimensional (2D) maps such as topological maps (Garg et al, 2017 ; Liao et al, 2017 ; Pronobis and Rao, 2017 ; Luperto and Amigoni, 2018 ; Wang et al, 2018a ) and occupancy grid maps (Goeddel and Olson, 2016 ; Sünderhauf et al, 2016 ; Himstedt and Maehle, 2017 ; Brucker et al, 2018 ; Posada et al, 2018 ; Rangel et al, 2019 ), were also conducted. Wang et al ( 2018a ) proposed a method that constructed a topological semantic map to guide object search.…”

Section: Related Workmentioning

confidence: 99%

“…Vocabulary used in daily human life depends on the environment a person is in, such as their home or office; a robot is unable to completely understand this, including words used to describe it, because the symbol system itself is a dynamic one (Taniguchi et al, 2016c). Many previous studies on semantic mapping (Kostavelis and Gasteratos, 2015;Goeddel and Olson, 2016;Sünderhauf et al, 2016;Himstedt and Maehle, 2017;Brucker et al, 2018;Posada et al, 2018;Rangel et al, 2019) have been conducted based on the assumption that a list of labels such as place names can be used as pre-existing knowledge; thus, they have been unable to estimate the meaning of place understood by a robot when is given a command including an unknown place name like "Joseph's room." To deal with various environments by adapting semantically to them, and to collaborate with people, a semantic mapping method that can deal with unknown words uttered by users is crucial for service robots used in daily life.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments

et al. 2019

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An autonomous robot performing tasks in a human environment needs to recognize semantic information about places. Semantic mapping is a task in which suitable semantic information is assigned to an environmental map so that a robot can communicate with people and appropriately perform tasks requested by its users. We propose a novel statistical semantic mapping method called SpCoMapping, which integrates probabilistic spatial concept acquisition based on multimodal sensor information and a Markov random field applied for learning the arbitrary shape of a place on a map.SpCoMapping can connect multiple words to a place in a semantic mapping process using user utterances without pre-setting the list of place names. We also develop a nonparametric Bayesian extension of SpCoMapping that can automatically estimate an adequate number of categories. In the experiment in the simulation environments, we showed that the proposed method generated better semantic maps than previous semantic mapping methods; our semantic maps have categories and shapes similar to the ground truth provided by the user. In addition, we showed that SpCoMapping could generate appropriate semantic maps in a real-world environment.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments

et al. 2019

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“…: room size, place category, and objects) of the mapped environments at a metric or topological level [1], [2]. Many methods proposed in recent works use CNNs (Convolutional Neural Networks) to recognize semantic properties through classification, object detection and semantic segmentation in images and integrate the results with metric or topological maps in several different approaches [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

“…Some works use a pre-trained CNN and perform transfer learning, fine-tuning it to recognize task-specific semantic information in the images [3], [4], [8], [16]. Others use a pre-trained CNN to perform the recognition of semantic properties without any changes or retraining [6], [14], [9], [12], [13], [15].…”

Section: Introductionmentioning

confidence: 99%

Topological Semantic Mapping by Consolidation of Deep Visual Features

Sousa,

Bassani

2021

Preprint

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Many works in the recent literature introduce semantic mapping methods that use CNNs (Convolutional Neural Networks) to recognize semantic properties in images. The types of properties (eg.: room size, place category, and objects) and their classes (eg.: kitchen and bathroom, for place category) are usually predefined and restricted to a specific task. Thus, all the visual data acquired and processed during the construction of the maps are lost and only the recognized semantic properties remain on the maps. In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN, the GoogLeNet, from 2D images captured in multiple views of the environment as the robot operates, to create consolidated representations of visual features acquired in the regions covered by each topological node. These consolidated representations allow flexible recognition of semantic properties of the regions and use in a range of visual tasks. The experiments, performed using a real-world indoor dataset, showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results. The objects are classified using the classification layer of GoogLeNet, without retraining, and the place categories are recognized using a shallow Multilayer Perceptron.

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“…The National Institute of Informatics / SOKENDAI (The Graduate University for Advanced Studies), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan desirable for robots to acquire place categories and vocabulary autonomously based on their experience because it is difficult to manually design spatial knowledge in advance. Related research in the fields of semantic mapping and place categorization (Pronobis and Jensfelt 2012;Kostavelis and Gasteratos 2015;Sünderhauf et al 2016;Landsiedel et al 2017;Rangel et al 2018) has attracted considerable interest in recent years. However, conventional approaches in most of these studies are limited insofar as the robots cannot learn unknown words and unknown place categories without preset vocabulary and categories.…”

Section: Introductionmentioning

confidence: 99%

Improved and scalable online learning of spatial concepts and language models with mapping

et al. 2020

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We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.

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Automatic semantic maps generation from lexical annotations

Cited by 13 publications

References 38 publications

Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments

Semantic Mapping Based on Spatial Concepts for Grounding Words Related to Places in Daily Environments

Topological Semantic Mapping by Consolidation of Deep Visual Features

Improved and scalable online learning of spatial concepts and language models with mapping

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