Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method

Zhang, Qianyi; Li, Ye; Zheng, Cong; Zhang, Wenjun

doi:10.1109/access.2021.3088541

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…A Rao-Blackwellized particle filter was used in [172] to solve the Monocular SLAM problem with a special weighting method. Zhang and Li [173] attempted to combine a probabilistic architecture with graph theory. Indeed, the authors proposed a Bathymetric Particle Filter mixed with graph-based theory.…”

Section: Localization With Filter-based Methodsmentioning

confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

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The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

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Section: Localization With Filter-based Methodsmentioning

confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…In recent years, terrain matching navigation based on particle-filtering technology has been applied [19,20]. The corresponding algorithms used in this process introduce the idea of probability estimation, which can effectively compensate for the shortcomings of geomagnetic matching algorithms based on correlation measurement.…”

Section: Introductionmentioning

confidence: 99%

Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology

Yu,

Li,

Yang

et al. 2023

JMSE

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The geomagnetic field constitutes a massive fingerprint database, and its unique structure provides potential position correction information. In recent years, particle filter technology has received more attention in the context of robot navigation. However, particle degradation and impoverishment have constrained navigation systems’ performance. This paper transforms particle filtering into a particle-matching positioning problem and proposes a geomagnetic localization method based on an adaptive fission particle filter. This method employs particle-filtering technology to construct a geomagnetic matching positioning model. Through adaptive particle fission and sampling, the problem of particle degradation and impoverishment in traditional particle filtering is solved, resulting in improved geomagnetic matching positioning accuracy. Finally, the proposed method was tested in a sea trial, and the results show that the proposed method has a lower positioning error than traditional particle-filtering and intelligent particle-filtering algorithms. Under geomagnetic map conditions, an average positioning accuracy of about 546.44 m is achieved.

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TTT SLAM: A feature-based bathymetric SLAM framework

Zhang,

Kim

2024

Ocean Engineering

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Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method

Cited by 7 publications

References 24 publications

Localization and Mapping for Self-Driving Vehicles: A Survey

Localization and Mapping for Self-Driving Vehicles: A Survey

Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology

TTT SLAM: A feature-based bathymetric SLAM framework

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