Ground Vehicle Localization With Particle Filter Based On Simulated Road Marking Image

Shipitko, Oleg; Grigoryev, Anton

doi:10.7148/2018-0341

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…In [36], the authors extracted two types of linear features, edges, and ridges by using a Hough transform. Similarly, authors of [37] have used a monocular camera to extract lanes' markings and pedestrian crossing lines. Polylines approximate these features.…”

Section: Semantic Featuresmentioning

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: Semantic Featuresmentioning

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

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“…The problem of detection end-to-end curves limited curvature is in high demand. An example is lane markings detection for map-relative unmanned ground vehicle localization [15,16,17] or building a lane map [18]. Similar problem is solved by unmanned aerial vehicles, but they detect roads [19] in optical aerial images and it is also take place in system for controlling trajectories of the agricultural combine harvester [20].…”

Section: Introductionmentioning

confidence: 99%

A method of detecting end-to-end curves of limited curvature

Panfilova¹,

Aliev²,

Kunina

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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In this paper we consider a method for detecting end-to-end curves of limited curvature like the k-link polylines with bending angle between adjacent segments in a given range. The approximation accuracy is achieved by maximization of the quality function in the image matrix. The method is based on a dynamic programming scheme constructed over Fast Hough Transform calculation results for image bands. The proposed method asymptotic complexity is O(h⋅(w+h/k)⋅log(h/k)), where h and w are the image size, and k is the approximating polyline links number, which is an analogue of the complexity of the fast Fourier transform or the fast Hough transform. We also show the results of the proposed method on synthetic and real data.

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