Fast and accurate localization and mapping method for self-driving vehicles based on a modified clustering particle filter

Charroud, Anas; Moutaouakil, Karim El; Yahyaouy, Ali

doi:10.1007/s11042-022-14111-4

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…Charroud et al [46,47] have removed the ground plan of all the LiDAR scan to reduce a huge amount of points, and they have used a Fuzzy K-means clustering technique to extract relevant features from the LiDAR scan. An extension of this work [48] adds a downsampling method to speed up the calculation process of the Fuzzy K-means algorithm.…”

Section: Non-semantics Featuresmentioning

confidence: 99%

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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: Non-semantics Featuresmentioning

confidence: 99%

“…-Semantic -Probabilistic -General -Probabilistic Calculation -Voxelisation -Building facades -Poles -Consume a lot -Middle -Hard -Low [42,43], [44,46], [47,48], [51,52], [53,54], [49,55], [56,57], [58,59], [60].…”

mentioning

confidence: 99%

Localization and Mapping for Self-Driving Vehicles: A Survey

Charroud,

El Moutaouakil,

Palade

et al. 2024

Machines

Self Cite

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“…More information about the registration can be found in [ 27 ]. The authors of [ 41 ] proposed a method to solve localization and mapping based on the use of a clustering-modified particle filter that selects the best candidate positions using sigma point selection techniques that have the same concept as the unexposed Kalman filter. These points speed up the localization process and also improve accuracy to achieve excellent results.…”

Section: Related Workmentioning

confidence: 99%

Rapid Localization and Mapping Method Based on Adaptive Particle Filters

Charroud

Moutaouakil

Yahyaouy

et al. 2022

Sensors

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With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles toderive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods.

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“…That is why the work of Charroud et al [25] proposed using non-semantic features to help perform the measurement-update step in the particle filter. An extension of this work was presented in the article [26], where the authors proposed a modified clustering particle filter that selects relevant particles to calculate the position by using sigma-point selection. Moreover, another extension of the work in [25] is the article [27], where it was proposed to extend the work on particle filters by selecting only the 10 best particles around the real position and regenerating the particles around them.…”

Section: Introductionmentioning

confidence: 99%

XDLL: Explained Deep Learning LiDAR-Based Localization and Mapping Method for Self-Driving Vehicles

et al. 2023

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Self-driving vehicles need a robust positioning system to continue the revolution in intelligent transportation. Global navigation satellite systems (GNSS) are most commonly used to accomplish this task because of their ability to accurately locate the vehicle in the environment. However, recent publications have revealed serious cases where GNSS fails miserably to determine the position of the vehicle, for example, under a bridge, in a tunnel, or in dense forests. In this work, we propose a framework architecture of explaining deep learning LiDAR-based (XDLL) models that predicts the position of the vehicles by using only a few LiDAR points in the environment, which ensures the required fastness and accuracy of interactions between vehicle components. The proposed framework extracts non-semantic features from LiDAR scans using a clustering algorithm. The identified clusters serve as input to our deep learning model, which relies on LSTM and GRU layers to store the trajectory points and convolutional layers to smooth the data. The model has been extensively tested with short- and long-term trajectories from two benchmark datasets, Kitti and NCLT, containing different environmental scenarios. Moreover, we investigated the obtained results by explaining the contribution of each cluster feature by using several explainable methods, including Saliency, SmoothGrad, and VarGrad. The analysis showed that taking the mean of all the clusters as an input for the model is enough to obtain better accuracy compared to the first model, and it reduces the time consumption as well. The improved model is able to obtain a mean absolute positioning error of below one meter for all sequences in the short- and long-term trajectories.

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Fast and accurate localization and mapping method for self-driving vehicles based on a modified clustering particle filter

Cited by 10 publications

References 37 publications

Localization and Mapping for Self-Driving Vehicles: A Survey

Localization and Mapping for Self-Driving Vehicles: A Survey

Rapid Localization and Mapping Method Based on Adaptive Particle Filters

XDLL: Explained Deep Learning LiDAR-Based Localization and Mapping Method for Self-Driving Vehicles

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