Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud–Robot System

Kabir, Raihan; Watanobe, Yutaka; Islam, Rashedul; Naruse, Keitaro; Rahman, Md. Mostafizer

doi:10.3390/s22041352

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…The one-class support vector machine (SVM) algorithm was proposed for application in the cloud-based mobile robotic system in [57]. The presented system was composed of a robot local station and a cloud-based station.…”

Section: Unsupervised Learning For Obstacle Avoidance and Path Planningmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make use of data to learn how to perform specific tasks or make appropriate decisions. This paper presents a comprehensive survey of recent ML approaches that have been applied to the task of mobile robot control, and they are divided into the following: supervised learning, unsupervised learning, and reinforcement learning. The distinction of ML methods applied to wheeled mobile robots and to walking robots is also presented in the paper. The strengths and weaknesses of the compared methods are formulated, and future prospects are proposed. The results of the carried out literature review enable one to state the ML methods that have been applied to different tasks, such as the following: position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, learning to walk, and multirobot coordination. The survey allowed us to associate the most commonly used ML algorithms with mobile robotic tasks. There still exist many open questions and challenges such as the following: complex ML algorithms and limited computational resources on board a mobile robot; decision making and motion control in real time; the adaptability of the algorithms to changing environments; the acquisition of large volumes of valuable data; and the assurance of safety and reliability of a robot’s operation. The development of ML algorithms for nature-inspired walking robots also seems to be a challenging research issue as there exists a very limited amount of such solutions in the recent literature.

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Section: Unsupervised Learning For Obstacle Avoidance and Path Planningmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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“…The detected objects were all unknown, but again the classes were not determined. The authors of [13] addressed the problem of unknown object detection using a one-class support vector machine. Since the learning process was incremental, multiple robots were involved, connected to each other via a cloud-based station where all the processing took place.…”

Section: Unknown Object Detectionmentioning

confidence: 99%

Multiperspective Teaching of Unknown Objects via Shared-gaze-based Multimodal Human-Robot Interaction

Weber

Fuhl

Kasneci

et al. 2023

Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

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For successful deployment of robots in multifaceted situations, an understanding of the robot for its environment is indispensable. With advancing performance of state-of-the-art object detectors, the capability of robots to detect objects within their interaction domain is also enhancing. However, it binds the robot to a few trained classes and prevents it from adapting to unfamiliar surroundings beyond predefined scenarios. In such scenarios, humans could assist robots amidst the overwhelming number of interaction entities and impart the requisite expertise by acting as teachers. We propose a novel pipeline that effectively harnesses human gaze and augmented reality in a human-robot collaboration context to teach a robot novel objects in its surrounding environment. By intertwining gaze (to guide the robot's attention to an object of interest) with augmented reality (to convey the respective class information) we enable the robot to quickly acquire a significant amount of automatically labeled training data on its own. Training in a transfer learning fashion, we demonstrate the robot's capability to detect recently learned objects and evaluate the influence of different machine learning models and learning procedures as well as the amount of training data involved. Our multimodal approach proves to be an efficient and natural way to teach the robot novel objects based on a few instances and allows it to detect classes for which no training dataset is available. In addition, we make our dataset publicly available to the research community, which consists of RGB and depth data, intrinsic and extrinsic camera parameters, along with regions of interest.

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“…4a and 4d, is segmented. Then, it is examined using a scribble (detection) validation technique based on extracting a histogram of oriented gradient (HOG) features [35,40] along with a one-class SVM classifier [41,42]. Here the learning is achieved in a way using only positive examples [43,44], where scribble samples represent the positive examples, and the negative examples are all other non-text objects or original texts, which could be initially misdetected/misclassified as scribbles.…”

Section: Analyzing Non-textual Objectsmentioning

confidence: 99%

Semantic Document Layout Analysis of Handwritten Manuscripts

Jaha¹

2023

Computers, Materials &Amp; Continua

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A document layout can be more informative than merely a document's visual and structural appearance. Thus, document layout analysis (DLA) is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives. This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis (SDLA) by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts. The proposed SDLA approach enables the derivation of implicit information and semantic characteristics, which can be effectively utilized in dozens of practical applications for various purposes, in a way bridging the semantic gap and providing more understandable high-level document image analysis and more invariant characterization via absolute and relative labeling. This approach is validated and evaluated on a large dataset of Arabic handwritten manuscripts comprising complex layouts. The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts. It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional, reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

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Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud–Robot System

Cited by 15 publications

References 37 publications

A Survey of Machine Learning Approaches for Mobile Robot Control

A Survey of Machine Learning Approaches for Mobile Robot Control

Multiperspective Teaching of Unknown Objects via Shared-gaze-based Multimodal Human-Robot Interaction

Semantic Document Layout Analysis of Handwritten Manuscripts

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