Object Recognition and Semantic Mapping for Underwater Vehicles Using Sonar Data

2017 ◽  
Vol 91 (2) ◽  
pp. 279-289 ◽  
Author(s):  
Matheus dos Santos ◽  
Paulo Drews ◽  
Pedro Núñez ◽  
Silvia Botelho
Keyword(s):  
Object Recognition ◽  
Underwater Vehicles ◽  
Semantic Mapping

scholarly journals Object-Oriented 3D Semantic Mapping Based on Instance Segmentation

2019 ◽  
Vol 23 (4) ◽  
pp. 695-704 ◽  
Author(s):  
Jinxin Chi ◽  
◽  
Hao Wu ◽  
Guohui Tian
Keyword(s):  
Object Recognition ◽  
Object Oriented ◽  
Mapping Method ◽  
Service Robots ◽  
Semantic Mapping ◽  
Continuous Image ◽  
Cloud Models ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Instance Segmentation

Service robots gain both geometric and semantic information about the environment with the help of semantic mapping, providing more intelligent services. However, a majority of studies for semantic mapping thus far require priori knowledge 3D object models or maps with a few object categories that neglect separate individual objects. In view of these problems, an object-oriented 3D semantic mapping method is proposed by combining state-of-the-art deep-learning-based instance segmentation and a visual simultaneous localization and mapping (SLAM) algorithm, which helps robots not only gain navigation-oriented geometric information about the surrounding environment, but also obtain individually-oriented attribute and location information about the objects. Meanwhile, an object recognition and target association algorithm applied to continuous image frames is proposed by combining visual SLAM, which uses visual consistency between image frames to promote the result of object matching and recognition over continuous image frames, and improve the object recognition accuracy. Finally, a 3D semantic mapping system is implemented based on Mask R-CNN and ORB-SLAM2 frameworks. A simulation experiment is carried out on the ICL-NUIM dataset and the experimental results show that the system can generally recognize all the types of objects in the scene and generate fine point cloud models of these objects, which verifies the effectiveness of our algorithm.

scholarly journals Design, Analysis and Development of Autonomous Underwater vehicle

2019 ◽  
Vol 9 (2) ◽  
pp. 3805-3810
Keyword(s):  
Object Recognition ◽  
Motion Control ◽  
Water Surface ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Heading Control ◽  
Underwater Object ◽  
Institute Of Technology

Autonomous Underwater Vehicles (AUV) are slowly operated unmanned robots which Capable of propelling on pre-defined mission tracks independently under the water surface and are frequently used for oceanographic exploration, bathymetric surveys and defense applications. This AUV can perform underwater object recognition and obstacle avoidance with the use of appropriate sensors and devices. Vidyut is a miniature AUV developed at Sri Sairam Institute of Technology. The vehicle is equipped with six thrusters which allow for motion control in 6 Dof and has a non-conventional single hull heavy bottom hydrodynamic design. This paper discusses different aspects of the vehicle's unique design. The output of the Arduino Uno controller has been discussed for continuous depth and heading control.

scholarly journals RGBD object recognition and visual texture classification for indoor semantic mapping

2012 ◽  
Author(s):  
David Filliat ◽  
Emmanuel Battesti ◽  
Stephane Bazeille ◽  
Guillaume Duceux ◽  
Alexander Gepperth ◽  
...  
Keyword(s):  
Object Recognition ◽  
Texture Classification ◽  
Semantic Mapping ◽  
Visual Texture

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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