Object recognition for SLAM in floor environments using a depth sensor

Chae, Hee-Won; Park, Chansoo; Yu, Hyejun; Song, Jae-Bok

doi:10.1109/urai.2016.7734070

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…In our previous work [11], the plane element was selected as a criterion for histogram variation to understand the relationship between the histogram and each surface element, since the histogram of the plane element has a very unique form compared to that of a sphere or cylinder. Fig.…”

Section: Surface Representation Based On Point Feature Histogrammentioning

confidence: 99%

“…In this study, we propose a new approach to object recognition based on the proposed surface component ratio histogram (SCRH) to address the challenging issues of RGB-D SLAM for the navigation of a cleaning robot. As an extension of our previous work [11], we adopted the well-known fast point feature histogram (FPFH) [12,13] for surface representation. Object recognition in this study implies recognizing whether the object's surfaces have previously been seen rather than simply identifying objects.…”

Section: Introductionmentioning

confidence: 99%

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Onboard Real-time Object Surface Recognition for a Small Indoor Mobile Platform Based on Surface Component Ratio Histogram

Chae

Song

2019

Int. J. Control Autom. Syst.

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Since a RGB-D sensor provides rich information about the scene, various object recognition schemes and low-level image descriptors can be used to improve the SLAM performance. However, a cleaning robot, which is usually flat and thus the camera is close to the floor, usually only has a partial view of the objects in front of the camera; therefore, conventional object recognition schemes based on the complete view of objects are not suitable. To address this problem, we introduce a novel object surface recognition algorithm based on the proposed surface component ratio histogram (SCRH). SCRH is a surface descriptor which describes the geometrical shape of the partial view of the object. Without any pre-trained model of the objects, SCRH provides a way to deal with the unexpected objects which the robot encounters during the navigation. SCRH was evaluated using several objects lying on the floor of which the identities are not known in advance. The experimental results show that objects are successfully discriminated based on their surfaces and SCRH is robust for object surface recognition.

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Section: Surface Representation Based On Point Feature Histogrammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Onboard Real-time Object Surface Recognition for a Small Indoor Mobile Platform Based on Surface Component Ratio Histogram

Chae

Song

2019

Int. J. Control Autom. Syst.

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“…Blum, Springenberg, Wülfing, and Riedmiller (2012) proposed a convolutional k-means descriptor for object recognition in RGB-D data. Chae, Park, Yu, and Song (2016) proposed a way to recognize objects for simultaneous localisation and mapping (SLAM) based on an object-level descriptor using a depth sensor. Bo, Ren, and Fox (2013) proposed an unsupervised feature learning method for RGB-D data, and the features were employed for object recognition using linear support vector machines.…”

Section: Introductionmentioning

confidence: 99%

Prior knowledge-based deep learning method for indoor object recognition and application

Ding

Luo

et al. 2018

Systems Science & Control Engineering

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Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision. ARTICLE HISTORY

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Indoor Image Recognition and Classification via Deep Convolutional Neural Network

Afif

Ayachi

Said

et al. 2019

Smart Innovation, Systems and Technologies

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Object recognition for SLAM in floor environments using a depth sensor

Cited by 8 publications

References 6 publications

Onboard Real-time Object Surface Recognition for a Small Indoor Mobile Platform Based on Surface Component Ratio Histogram

Onboard Real-time Object Surface Recognition for a Small Indoor Mobile Platform Based on Surface Component Ratio Histogram

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor Image Recognition and Classification via Deep Convolutional Neural Network

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