scholarly journals Multifeature Image Indexing for Robot Localization in Textureless Environments

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 37
Author(s):  
Tran Duc Dung ◽  
Delowar Hossain ◽  
Shin-ichiro Kaneko ◽  
Genci Capi

Robot localization is an important task for mobile robot navigation. There are many methods focused on this issue. Some methods are implemented in indoor and outdoor environments. However, robot localization in textureless environments is still a challenging task. This is because in these environments, the scene appears the same in almost every position. In this work, we propose a method that can localize robots in textureless environments. We use Histogram of Oriented Gradients (HOG) and Speeded Up Robust Feature (SURF) descriptors together with Depth information to form a Depth-HOG-SURF multifeature descriptor, which is later used for image matching. K-means clustering is applied to partition the whole feature into groups that are collectively called visual vocabulary. All the images in the database are encoded using the vocabulary. The experimental results show a good performance of the proposed method.

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 85
Author(s):  
Annika Hoffmann

The detection and description of features is one basic technique for many visual robot navigation systems in both indoor and outdoor environments. Matched features from two or more images are used to solve navigation problems, e.g., by establishing spatial relationships between different poses in which the robot captured the images. Feature detection and description is particularly challenging in outdoor environments, and widely used grayscale methods lead to high numbers of outliers. In this paper, we analyze the use of color information for keypoint detection and description. We consider grayscale and color-based detectors and descriptors, as well as combinations of them, and evaluate their matching performance. We demonstrate that the use of color information for feature detection and description markedly increases the matching performance.


2019 ◽  
Vol 38 (5) ◽  
pp. 507-517 ◽  
Author(s):  
Oscar Martinez Mozos ◽  
Kazuto Nakashima ◽  
Hojung Jung ◽  
Yumi Iwashita ◽  
Ryo Kurazume

This paper presents several multi-modal 3D datasets for the problem of categorization of places. In this problem. a robotic agent should decide on the type of place/environment where it is located (residential area, forest, etc.) using information gathered by its sensors. In addition to the 3D depth information, the datasets include additional modalities such as RGB or reflectance images. The observations were taken in different indoor and outdoor environments in Fukuoka city, Japan. Outdoor place categories include forests, urban areas, indoor parking, outdoor parking, coastal areas, and residential areas. Indoor place categories include corridors, offices, study rooms, kitchens, laboratories, and toilets. The datasets are available to download at http://robotics.ait.kyushu-u.ac.jp/kyushu_datasets .


Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 359
Author(s):  
Ewa Brągoszewska

The Atmosphere Special Issue entitled “Health Effects and Exposure Assessment to Bioaerosols in Indoor and Outdoor Environments” comprises five original papers [...]


1979 ◽  
Vol 73 (4) ◽  
pp. 121-126 ◽  
Author(s):  
Natalie C. Barraga ◽  
Marcia E. Collins

The rationale for a comprehensive program in visual functioning is based upon an assumed interaction between: (a) functions performed by the visual system, (b) developmental visual tasks organized in keeping with perceptual/cognitive milestones, and (c) a variety of indoor and outdoor environments.


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