Robust Steering Control for Autonomous Homing and its Application in Visual Homing under Practical Conditions

Arunkumar, G.; Sabnis, Anupa; Vachhani, Leena

doi:10.1007/s10846-017-0561-2

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…At present, most of the business areas are connected to big data. Big data has a critical effect on different dimensions of businesses, for example, in terms of human resources management, research, and development [27,28]; management processes [27,[29][30][31]; business to business processes, advertising, and marketing [32,33]; mechanical assembling measures [34][35][36]; enterprises' operational performance [36,37]; policy-making [38]; and supply chain management [38,39]. In particular, Rabhi, Falih [27] referenced big data as a noteworthy factor of business measure management and HR cycles to help in the decision-making process.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Big Data’s Opportunities, Challenges, and Implications in Finance

et al. 2020

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One of the latest innovations in business and technology is the use of big data, as daily data are generated by billions of events. The big data issue is now considered in the accountants and finance professionals’ field as one of the most important sources for the analysis of financial products and services. This study is very innovative, with our research aiming to identify the opportunities, challenges, and implications of big data in the finance area. It is our purpose to find competitive advantages in terms of the extent to which big data brings visible benefits, also pointing out the challenges that a company may face in this field, such as cases of customers’ data security or customer satisfaction processes. The identification of this kind of dynamics allows us to draw conclusions on the advantages of big data based on these analyses and big data’s deep impact on finance. In particular, big data is now commonly used by financial institutions and banks for analytical purposes in financial market contexts. We conducted an exploratory survey of the existing literature to highlight such connections. In the last part of our study, we also propose directions for future research.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying Big Data’s Opportunities, Challenges, and Implications in Finance

et al. 2020

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“…Visual homing is a qualitative navigation technology. Compared to traditional SLAM (simultaneous localization and mapping) [6][7][8] techniques, visual homing does not require the robot to perform any self-localization or mapping, but only to compare the two panoramic images respectively captured at its current location and the reference home location, thus greatly simplifying the sources of both the hardware and the software [9][10][11]. Home vector is the only output of the visual homing approaches, which refers to the direction from the robot's current location to the home location.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Ji,

Zhu,

Wang

et al. 2019

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In this paper, we present an optimization mechanism for two popular landmark-based mobile robot visual homing algorithms (ALV and HiSS), called vector pre-assigned mechanism (VPM). VPM contains two branches, both of which can promote the homing performance effectively. In addition, to make the landmark distribution satisfy the equal distance assumption, a landmark optimization strategy is proposed based on imaging principle of the panoramic vision. Experiments on both panoramic image database and a real mobile robot have confirmed the effectiveness of the proposed methods.

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“…Instead, it only needs to continuously calculate the direction vector, known as the homing vector, pointing from the current location to the destination. In summary, the robot can achieve the homing task by solely repeating the following three steps: (1) relating the current panoramic image to the target panoramic image; (2) computing the homing vector; and (3) moving based on the calculated homing vector [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Three Landmark Optimization Strategies for Mobile Robot Visual Homing

Zhu

et al. 2018

Sensors

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Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies.

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Robust Steering Control for Autonomous Homing and its Application in Visual Homing under Practical Conditions

Cited by 9 publications

References 17 publications

Identifying Big Data’s Opportunities, Challenges, and Implications in Finance

Identifying Big Data’s Opportunities, Challenges, and Implications in Finance

Bulletin of the Polish Academy of Sciences: Technical Sciences

Three Landmark Optimization Strategies for Mobile Robot Visual Homing

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