Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Zhang, Jitong; Ren, Mingrong; Wang, Pu; Meng, Juan; Mu, Yuman

doi:10.3390/s20102790

Cited by 14 publications

(11 citation statements)

References 26 publications

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“…Similar to lidar, RGB cameras [47] are used to collect image information, extract features and construct scenes through algorithms to determine their positions from them. The accuracy of the position solution is within 20 cm.…”

Section: Rgb Cameramentioning

confidence: 99%

A Survey of Robot Swarms’ Relative Localization Method

Chen

Yin

Niu

2022

Sensors

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For robot swarm applications, accurate positioning is one of the most important requirements for avoiding collisions and keeping formations and cooperation between individuals. However, in some worst cases, the GNSS (Global Navigation Satellite System) signals are weak due to the crowd being in a swarm or blocked by a forest, mountains, and high buildings in the environment. Thus, relative localization is an indispensable way to provide position information for the swarm. In this paper, we review the status and development of relative localization. It is first assessed that relative localization to obtain spatio-temporal relationships between individuals is necessary to achieve the stable operation of the group. After analyzing typical relative localization systems and algorithms from the perspective of functionality and practicality, this paper concludes that the UWB-based (ultra wideband) system is suitable for the relative localization of robots in large-scale applications. Finally, after analyzing the current challenges in the field of fully distributed localization for robotic swarms, a complete mechanism encompassing the relative localization process and the relationship between local and global localization that can be a possible direction for future research is proposed.

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Section: Rgb Cameramentioning

confidence: 99%

A Survey of Robot Swarms’ Relative Localization Method

Chen

Yin

Niu

2022

Sensors

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“…11 illustrated a combination between MM and PDR techniques. Map-matching algorithms use a priori knowledge of geographic characteristics to improve a target device's navigation solution by locating the target's real 2D or 3D coordinates inside the movement area [60]. This process is performed via employing machine-learning algorithms including pattern recognition/matching algorithms.…”

Section: F Map-matching Techniquementioning

confidence: 99%

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Asaad¹,

Maghdid²

2021

Preprint

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<p>The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since, most of the people daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT enabled technologies in health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated.</p>

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“…Copious studies have been done on visual-inertial SLAM along with various applications that have since been on a continuum to full maturity [ 9 , 10 , 11 ]. Until today, scholars are still endeavoring to bring it to perfection with novel ideas and approaches [ 12 , 13 , 14 , 15 , 16 ]. Incipient methods fuse inertial and visual measurements loosely where the two types of information are processed, filtered, and used for estimation all separately [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

Wang

et al. 2020

Sensors

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Visual-inertial navigation systems are credited with superiority over both pure visual approaches and filtering ones. In spite of the high precision many state-of-the-art schemes have attained, yaw remains unobservable in those systems all the same. More accurate yaw estimation not only means more accurate attitude calculation but also leads to better position estimation. This paper presents a novel scheme that combines visual and inertial measurements as well as magnetic information for suppressing deviation in yaw. A novel method for initializing visual-inertial-magnetic odometers, which recovers the directions of magnetic north and gravity, the visual scalar factor, inertial measurement unit (IMU) biases etc., has been conceived, implemented, and validated. Based on non-linear optimization, a magnetometer cost function is incorporated into the overall optimization objective function as a yawing constraint among others. We have done extensive research and collected several datasets recorded in large-scale outdoor environments to certify the proposed system’s viability, robustness, and performance. Cogent experiments and quantitative comparisons corroborate the merits of the proposed scheme and the desired effect of the involvement of magnetic information on the overall performance.

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Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Cited by 14 publications

References 26 publications

A Survey of Robot Swarms’ Relative Localization Method

A Survey of Robot Swarms’ Relative Localization Method

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

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