A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle

Ahn, Kyung-Jae; Kang, Yeonsik

doi:10.1155/2019/3680181

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…In comparison with other techniques, the performance of the proposed technique is better than the previous as investigated in the literature. A number of localization approaches are presented by the researchers [14,43,51,52]. Each approach focused on the localization performance, but a limited number of aspects are considered such as most of them focused only on the accuracy of localization.…”

Section: Simulation Discussion and Comparisonmentioning

confidence: 99%

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Ullah

Zhu

et al. 2020

Wireless Communications and Mobile Computing

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Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

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Section: Simulation Discussion and Comparisonmentioning

confidence: 99%

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Ullah

Zhu

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The choice of a particular type of sensor depends on the application’s conditions. Current vehicle detection and classification systems work on the principle of ultrasonic sensors, acoustic sensors [ 1 ], infrared sensors [ 2 ], inductive loops [ 3 ], magnetic sensors [ 4 ], video sensors [ 5 ], laser sensors [ 6 ], and microwave radars [ 7 ]. The authors in [ 8 , 9 ] divide sensors into invasive and non-invasive.…”

Section: Introductionmentioning

confidence: 99%

Application of Wireless Magnetic Sensors in the Urban Environment and Their Accuracy Verification

Čulík

Štefancová

Hrudkay

2023

Sensors

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In a smart city, sensors are essential elements—the source of up-to-date traffic information. This article deals with magnetic sensors connected to wireless sensor networks (WSNs). They have a low investment cost, a long lifetime, and easy installation. However, it is still necessary to disturb the road surface locally during their installation. All road lanes to and from the city center of Žilina have sensors that send data at five-minute intervals. They send up-to-date information about the traffic flow’s intensity, speed, and composition. The LoRa network ensures the data transmission, but in the event of failure, the 4G/LTE modem realizes the backup transmission. The disadvantage of this application of sensors is their accuracy. The research task was to compare the outputs from the WSN with a traffic survey. The appropriate method for the traffic survey on the selected road profile is a video recording and speed measurement using the Sierzega radar. The results show distorted values, mainly for short intervals. The most accurate output from magnetic sensors is the number of vehicles. On the other hand, traffic flow composition and speed measurement are relatively inaccurate because it is not easy to identify vehicles based on dynamic length. Another problem with sensors is frequent communication outages, which cause an accumulation of values after the outage ends. The secondary objective of the paper is to describe the traffic sensor network and its publicly accessible database. In the end, there are several proposals for data usage.

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“…Another solution adopted by some researchers consists in probabilistic localization, which can be performed using different sensors such as LiDAR [9], cameras [10,11] or magnetometers [12]. One of the most common method for this kind of solution is Monte Carlo Localization (MCL) [4].…”

Section: Introductionmentioning

confidence: 99%

“…As an improvement for MCL, Adaptive Monte Carlo Localization (AMCL) outperforms classic (MCL) [13] as it uses Kullback-Leibler Distance (KLD) sampling to make the filter converge faster. Particle filter localization methods can be applied to autonomous vehicles, like in [9], that uses a 2D LiDAR and a map with the features of the road.…”

Section: Introductionmentioning

confidence: 99%

Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

Miguel

García

Armingol

2020

Sensors

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This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.

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A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle

Cited by 10 publications

References 8 publications

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Application of Wireless Magnetic Sensors in the Urban Environment and Their Accuracy Verification

Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

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