Semantic labeling for indoor topological mapping using a wearable catadioptric system

Rituerto, Alejandro; Murillo, Ana C.; Guerrero, J. J.

doi:10.1016/j.robot.2012.10.002

Cited by 34 publications

(19 citation statements)

References 31 publications

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“…Also, Rituerto et al [121] propose the use of a semantic topological map created from the global appearance description of a set of images captured by an omnidirectional vision system. These authors propose the use of a gistbased descriptor to obtain a compact representation of the scene and estimate the similarity between two locations based on the Euclidean distance between descriptors.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

2017

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Nowadays, the field of mobile robotics is experiencing a quick evolution, and a variety of autonomous vehicles is available to solve different tasks. The advances in computer vision have led to a substantial increase in the use of cameras as the main sensors in mobile robots. They can be used as the only source of information or in combination with other sensors such as odometry or laser. Among vision systems, omnidirectional sensors stand out due to the richness of the information they provide the robot with, and an increasing number of works about them have been published over the last few years, leading to a wide variety of frameworks. In this review, some of the most important works are analysed. One of the key problems the scientific community is addressing currently is the improvement of the autonomy of mobile robots. To this end, building robust models of the environment and solving the localization and navigation problems are three important abilities that any mobile robot must have. Taking it into account, the review concentrates on these problems; how researchers have addressed them by means of omnidirectional vision; the main frameworks they have proposed; and how they have evolved in recent years.

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Section: Journal Of Sensorsmentioning

confidence: 99%

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

2017

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“…In particular, acquiring models of the environment for long term operation is a subject of great interest, since it provides intelligent systems with higher autonomy [ 11 , 12 ]. Additionally, enhancing those models with semantic information is a key element for human–computer interaction [ 13 , 14 , 15 , 16 , 17 ].…”

Section: Related Workmentioning

confidence: 99%

Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera

Rituerto

Andreasson

Murillo

et al. 2016

Sensors

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Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set.

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“…We used the OmniCam dataset presented in [15], and used for metric and topological indoor navigation. The dataset consists of two different sequences of images acquired in the same environment.…”

Section: Lis Image Tessellation Analysismentioning

confidence: 99%

Line image signature for scene understanding with a wearable vision system

Rituerto

Murillo

Guerrero

2013

Proceedings of the 4th International SenseCam &Amp; Pervasive Imaging Conference on - SenseCam '13

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Wearable computer vision systems provide plenty of opportunities to develop human assistive devices. This work contributes on visual scene understanding techniques using a helmet-mounted omnidirectional vision system. The goal is to extract semantic information of the environment, such as the type of environment being traversed or the basic 3D layout of the place, to build assistive navigation systems. We propose a novel line-based image global descriptor that encloses the structure of the scene observed. This descriptor is designed with omnidirectional imagery in mind, where observed lines are longer than in conventional images. Our experiments show that the proposed descriptor can be used for indoor scene recognition comparing its results to state-of-theart global descriptors. Besides, we demonstrate additional advantages of particular interest for wearable vision systems: higher robustness to rotation, compactness, and easier integration with other scene understanding steps.

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Semantic labeling for indoor topological mapping using a wearable catadioptric system

Cited by 34 publications

References 31 publications

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera

Line image signature for scene understanding with a wearable vision system

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