Mobile Robot Self-Localization Using Omnidirectional Vision with Feature Matching from Real and Virtual Spaces

Lin, Huei-Yung; He, Chien-Hsing

doi:10.3390/app11083360

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…The mechanical prototype proposed in this paper is not able to develop self-localization [36]. The localization of the mobile robot is determined with an external measurement test bench, the general scheme and view of which are presented in Figure 12.…”

Section: External Measurement Test Benchmentioning

confidence: 99%

A Mobile Robot with Omnidirectional Tracks—Design and Experimental Research

Fiedeń

Bałchanowski

2021

Applied Sciences

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This article deals with the design and testing of mobile robots equipped with drive systems based on omnidirectional tracks. These are new mobile systems that combine the advantages of a typical track drive with the advantages of systems equipped with omnidirectional Mecanum wheels. The omnidirectional tracks allow the robot to move in any direction without having to change the orientation of its body. The mobile robot market (automated construction machinery, mobile handle robots, mobile platforms, etc.) constantly calls for improvements in the manoeuvrability of vehicles. Omnidirectional drive technology can meet such requirements. The main aim of the work is to create a mobile robot that is capable of omnidirectional movement over different terrains, and also to conduct an experimental study of the robot’s operation. The paper presents the construction and principles of operation of a small robot equipped with omnidirectional tracks. The robot’s construction and control system, and also a prototype made with FDM technology, are described. The trajectory parameters of the robot’s operation along the main and transverse axes were measured on a test stand equipped with a vision-based measurement system. The results of the experimental research became the basis for the development and experimental verification of a static method of correcting deviations in movement trajectory.

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Section: External Measurement Test Benchmentioning

confidence: 99%

A Mobile Robot with Omnidirectional Tracks—Design and Experimental Research

Fiedeń

Bałchanowski

2021

Applied Sciences

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“…The omnidirectional vision systems have been widely adopted in the mobile robot research for simultaneous localization and mapping (SLAM) [ 3 ]. In addition to the localization accuracy, it is also an important issue to consider the applicability to a wide range of real-world scenarios.…”

Section: Related Workmentioning

confidence: 99%

Self-Localization of Mobile Robots Using a Single Catadioptric Camera with Line Feature Extraction

Lin

Chung

Wang

2021

Sensors

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This paper presents a novel self-localization technique for mobile robots using a central catadioptric camera. A unified sphere model for the image projection is derived by the catadioptric camera calibration. The geometric property of the camera projection model is utilized to obtain the intersections of the vertical lines and ground plane in the scene. Different from the conventional stereo vision techniques, the feature points are projected onto a known planar surface, and the plane equation is used for depth computation. The 3D coordinates of the base points on the ground are calculated using the consecutive image frames. The derivation of motion trajectory is then carried out based on the computation of rotation and translation between the robot positions. We develop an algorithm for feature correspondence matching based on the invariability of the structure in the 3D space. The experimental results obtained using the real scene images have demonstrated the feasibility of the proposed method for mobile robot localization applications.

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“…Its main objective is to find a path for the robot such that every location in the workspace can be visited. The techniques are commonly adopted to explore the environment or structure of interest comprehensively for inspection purposes [ 2 ]. In the past few decades, CCPP algorithms have attracted much attention in the robotics research community due to their practical applications to cleaning, mining, agriculture and rescue robots, etc.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration

Lin

Huang

2021

Sensors

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In mobile robotics research, the exploration of unknown environments has always been an important topic due to its practical uses in consumer and military applications. One specific interest of recent investigation is the field of complete coverage and path planning (CCPP) techniques for mobile robot navigation. In this paper, we present a collaborative CCPP algorithms for single robot and multi-robot systems. The incremental coverage from the robot movement is maximized by evaluating a new cost function. A goal selection function is then designed to facilitate the collaborative exploration for a multi-robot system. By considering the local gains from the individual robots as well as the global gain by the goal selection, the proposed method is able to optimize the overall coverage efficiency. In the experiments, our CCPP algorithms are carried out on various unknown and complex environment maps. The simulation results and performance evaluation demonstrate the effectiveness of the proposed collaborative CCPP technique.

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Mobile Robot Self-Localization Using Omnidirectional Vision with Feature Matching from Real and Virtual Spaces

Cited by 9 publications

References 29 publications

A Mobile Robot with Omnidirectional Tracks—Design and Experimental Research

A Mobile Robot with Omnidirectional Tracks—Design and Experimental Research

Self-Localization of Mobile Robots Using a Single Catadioptric Camera with Line Feature Extraction

Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration

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