Linear Features Observation Model for Autonomous Vehicle Localization

Shipitko, Oleg; Kibalov, Vladislav; Abramov, Maxim

doi:10.1109/icarcv50220.2020.9305434

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…Most researchers working with semantics try to extract contours, line segments, and objects in general. 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.…”

Section: Semantic Featuresmentioning

confidence: 99%

See 1 more Smart Citation

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%

“…So, all feature extraction algorithms we have already discussed are candidates for solving localization and mapping tasks. Features that are widely used for the purpose of creating a map are lanes [21,22,36], buildings [22,28,33], curbs [24,25,33], poles [26][27][28][29][30]32,33], or a combination of these. These maps are referred to as feature maps.…”

mentioning

confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

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“…An autonomous logistic vehicle (Fig. 7) is equipped with the Particle Filter-based localization system described in [31].…”

Section: Prior Distribution Refinementmentioning

confidence: 99%

Prior Distribution Refinement for Reference Trajectory Estimation With the Monte Carlo-Based Localization Algorithm

Griguletskii

Shipitko²,

Abramov

et al. 2023

IEEE Access

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A robot localization problem demands a fair comparison of the positioning algorithms. A reference trajectory of the robot's movement is needed to estimate errors and evaluate a quality of the localization. In this article, we propose the Prior Distribution Refinement method for generating a reference trajectory of a mobile robot with the Monte Carlo-based localization system. The proposed approach can be applied for both indoor and outdoor environments of an arbitrary size without the need for expensive position tracking sensors or intervention in the testing infrastructure. The reference trajectory is generated by running the algorithm over a so-called Particles' Transition Graph, obtained from a resampling stage of Monte Carlo localization. The prior distribution of particles is then refined by forward-backward propagation through the graph and exploring the connections between particles. The Viterbi algorithm is applied afterwards to generate a reference trajectory based on refined particles' distribution. We demonstrate that such an approach is capable of generating accurate estimates of a mobile robot's position and orientation with the only requirement of moderate quality of localization system being used as a core algorithm for iterative optimization.

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“…In recent years, line matching has received significant attention in many fields, such as image registration [11], 3D reconstruction [12,13], and location estimation [14][15][16]. Points in feature-point matching cannot characterize the geometric and structural information of the scene since their distribution is not restricted to the edges.…”

Section: Introductionmentioning

confidence: 99%

The Plumb-Line Matching Algorithm for UAV Oblique Photographic Photos

Zhang,

Sun,

Gao

et al. 2023

Remote Sensing

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Building facades has always been a challenge for feature matching in oblique photogrammetry due to weak textures, non-Lambertian objects, severe occlusion, and distortion. Plumb lines are essential building geometry structural feature lines in building facades, which show strong spatial relevance to these problems. Achieving plumb line matching has great application potential for optimizing the process and products of oblique photogrammetry. Thus, we proposed a novel matching algorithm for plumb lines based on spatial and color hybrid constraints according to its central projection imaging characteristics. Firstly, based on vanishing point theory, the plumb lines from photos were back-calculated to determine the matching target set; secondly, the property of its large elevation ranges was exploited to calculate the homonymous points as spatial constraints by projecting plumb lines onto the stratified spatial planes; thirdly, the neighboring primary colors on both sides of the plumb lines were extracted as feature descriptors and compared by colorimetry; then, the greedy strategy was employed to successively filter out the locally optimal solutions satisfying the spatial and color hybrid constraints to complete the initial matching; finally, the intersection-over-union analysis of the solution plane and the verticalness evaluation of the matching results were implemented to eliminate errors. The results show that the proposed algorithm can achieve an average accuracy of 97.29% and 78.41% in the forward and lateral overlap experiments from multi-scenes, respectively, displaying a strong adaptability to poor texture, inconsistency, and distortion. In conclusion, thanks to the plumb-line-oriented matching strategy, this algorithm owns inherent advantages in theory and computational complexity. It is suitable for all building-oriented oblique photogrammetry tasks and is highly worthy of promotion and application.

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Linear Features Observation Model for Autonomous Vehicle Localization

Cited by 5 publications

References 27 publications

Localization and Mapping for Self-Driving Vehicles: A Survey

Localization and Mapping for Self-Driving Vehicles: A Survey

Prior Distribution Refinement for Reference Trajectory Estimation With the Monte Carlo-Based Localization Algorithm

The Plumb-Line Matching Algorithm for UAV Oblique Photographic Photos

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