Semantic Mapping with Omnidirectional Vision

Posada, Luis Felipe; Velásquez-López, Alejandro; Hoffmann, Frank; Bertram, Torsten

doi:10.1109/icra.2018.8461165

Cited by 16 publications

(7 citation statements)

References 21 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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“…Pais et al [30] used reinforcement learning to predict pedestrians’ positions by projecting the 3D bounding boxes of pedestrians onto panoramic images. There are also methods which only use adequate planar convolutional filters to fit the rotation distortions [31,32].…”

Section: Related Workmentioning

confidence: 99%

Grid Based Spherical CNN for Object Detection from Panoramic Images

2019

Sensors

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Recently proposed spherical convolutional neural networks (SCNNs) have shown advantages over conventional planar CNNs on classifying spherical images. However, two factors hamper their application in an objection detection task. First, a convolution in S2 (a two-dimensional sphere in three-dimensional space) or SO(3) (three-dimensional special orthogonal group) space results in the loss of an object’s location. Second, overlarge bandwidth is required to preserve a small object’s information on a sphere because the S2/SO(3) convolution must be performed on the whole sphere, instead of a local image patch. In this study, we propose a novel grid-based spherical CNN (G-SCNN) for detecting objects from spherical images. According to input bandwidth, a sphere image is transformed to a conformal grid map to be the input of the S2/SO3 convolution, and an object’s bounding box is scaled to cover an adequate area of the grid map. This solves the second problem. For the first problem, we utilize a planar region proposal network (RPN) with a data augmentation strategy that increases rotation invariance. We have also created a dataset including 600 street view panoramic images captured from a vehicle-borne panoramic camera. The dataset contains 5636 objects of interest annotated with class and bounding box and is named as WHU (Wuhan University) panoramic dataset. Results on the dataset proved our grid-based method is extremely better than the original SCNN in detecting objects from spherical images, and it outperformed several mainstream object detection networks, such as Faster R-CNN and SSD.

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“…Omnidirectional images represent the type of images that covers almost the entire sphere or at least a full horizontal plane, therefore containing much more information than the conventional one. Duo to its various advantages, omnidirectional images has been widely used in several research fields such as: Person detection and tracking [1], robot navigation [2], 3D scene reconstruction [3], line image extraction [4] etc. essentially for the 360° field of view provided by a such image.…”

Section: Introductionmentioning

confidence: 99%

Edge detection algorithm for omnidirectional images, based on superposition laws on Blach’s sphere and quantum entropy

Ezzaki

Benkhedra

Ansari

et al. 2021

ELCVIA

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This paper presents an edge detection algorithm for omnidirectional images based on superposition law on Bloch's sphere and quantum local entropy. Omnidirectional vision system has become an essential tool in computer vision, due to its large field of view. However, classical image processing algorithms are not suitable to be applied directly on this type of images without taking into account the spatial information around each pixel. To show the performance of the proposed method, a set of experimentation was done on synthetic and real images devoted to agriculture applications. Later, Fram & Deutsh criterion has been adopted to evaluate its performance against three algorithms proposed in the literature and developed for omnidirectional images. The results show a better performance in term of edge quality, edge community and sensibility to noise.

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Semantic Mapping with Omnidirectional Vision

Cited by 16 publications

References 21 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

Grid Based Spherical CNN for Object Detection from Panoramic Images

Edge detection algorithm for omnidirectional images, based on superposition laws on Blach’s sphere and quantum entropy

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