Positioning System for an Electric Autonomous Vehicle Based on the Fusion of Multi-GNSS RTK and Odometry by Using an Extented Kalman Filter

Tradacete, Miguel; Sáez, A. Bernabé; Arango, J. Felipe; Huélamo, Carlos Gómez; Revenga, P.; Barea, Rafael; López-Guillén, Elena; Bergasa, Luis M.

doi:10.1007/978-3-319-99885-5_2

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…The local navigation module commands the translational velocity and the rotation angle of the front wheels and uses the odometry and the readings from a LIDAR Velodyne (VLP-16) located on top of the vehicle. More details about the car and the research carried out in other modules of Figure 1 can be seen in [50][51][52][53].…”

Section: Resultsmentioning

confidence: 99%

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

López

Sánchez-Vilariño

Sanz

et al. 2020

Sensors

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Most autonomous car control frameworks are based on a middleware layer with several independent modules that are connected by an inter-process communication mechanism. These modules implement basic actions and report events about their state by subscribing and publishing messages. Here, we propose an executive module that coordinates the activity of these modules. This executive module uses hierarchical interpreted binary Petri nets (PNs) to define the behavior expected from the car in different scenarios according to the traffic rules. The module commands actions by sending messages to other modules and evolves its internal state according to the events (messages) received. A programming environment named RoboGraph (RG) is introduced with this architecture. RG includes a graphical interface that allows the edition, execution, tracing, and maintenance of the PNs. For the execution, a dispatcher loads these PNs and executes the different behaviors. The RG monitor that shows the state of all the running nets has proven to be very useful for debugging and tracing purposes. The whole system has been applied to an autonomous car designed for elderly or disabled people.

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Section: Resultsmentioning

confidence: 99%

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

López

Sánchez-Vilariño

Sanz

et al. 2020

Sensors

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“…In the case of semi-urban and residential areas, precision positioning error is less affected by non-line-of-sight reception and multipath interference. Therefore, in these areas high-precision positioning is, as a rule, implemented with the use of GNSS receiver and differential correction technologies of navigation data [30][31][32][33]. Often, for transport purposes, researchers consider as a rover high-priced professional receivers that provide accurate positioning at the level of centimeters and millimeters.…”

Section: Related Studiesmentioning

confidence: 99%

“…For example, in [30] it was used an expensive GNSS receiver of geodesic class Trimble R10 and as a result of kinematic measurements in urban residential area it was shown that multi-constellation GNSS in the selection of the optimal mask angle and data processing using RTK technology allows to achieve accuracy at the level of a few centimeters in harsh environments with tree canopies. In work [31] Topcon HyperPro+ was used as a rover and, having supplemented the obtained results with odometry data, the accuracy of navigation definitions was also obtained at centimeter level. Such solutions are relevant for navigation of unmanned moving objects (i.e.…”

Section: Related Studiesmentioning

confidence: 99%

Investigation of lane-level GNSS positioning of vehicle in urban area

Akhmedov¹,

Moldabekov²,

Yeryomin³

et al. 2021

J Appl Eng Science

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High quality GNSS (Global Navigation Satellite System) positioning can be useful for numerous engineering tasks, for example, in transport applications concerned to monitoring and optimization of road traffic. In this case lane-level positioning is a relevant task and its solution should satisfy a wide range of users, and thus should be low-cost and easy to use. In this paper the solution of accurate GNSS positioning of the car with the use of differential correction of navigation data in order to provide positioning by the lane level in urban areas of Kazakhstan is investigated. A smartphone is considered as low-cost navigation aid. Navigation data obtained using a smartphone were differentially corrected using the developed software of the Control System for Reference GNSS Station Network relative to one reference station. It is shown that using a smartphone as a navigation aid with further differential correction of data relative to one reference station allows positioning vehicles in motion with an error of 1.4 meters. The result is valuable as a basis for developing intelligent transportation systems.

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“…Fisheye camera was integrated in an open-source autonomous car prototype [33,34] as a complement to its main perception system, formed by a ZED camera, manufactured by StereoLabs, a VLP-16 LiDAR, manufactured by Velodyne, a HiPer Pro RTK-GPS receiver by TOPCON and odometry sensors by Kubler. Environment perception of the prototype is based on 3D semantic segmentation obtained from the fusion of LiDAR and segmented images, which is able to detect obstacles in a 3D environment [35].…”

Section: Application To a Real Fisheye Cameramentioning

confidence: 99%

Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

Sáez

Bergasa

López-Guillén

et al. 2019

Sensors

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The interest in fisheye cameras has recently risen in the autonomous vehicles field, as they are able to reduce the complexity of perception systems while improving the management of dangerous driving situations. However, the strong distortion inherent to these cameras makes the usage of conventional computer vision algorithms difficult and has prevented the development of these devices. This paper presents a methodology that provides real-time semantic segmentation on fisheye cameras leveraging only synthetic images. Furthermore, we propose some Convolutional Neural Networks(CNN) architectures based on Efficient Residual Factorized Network(ERFNet) that demonstrate notable skills handling distortion and a new training strategy that improves the segmentation on the image borders. Our proposals are compared to similar state-of-the-art works showing an outstanding performance and tested in an unknown real world scenario using a fisheye camera integrated in an open-source autonomous electric car, showing a high domain adaptation capability.

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Positioning System for an Electric Autonomous Vehicle Based on the Fusion of Multi-GNSS RTK and Odometry by Using an Extented Kalman Filter

Cited by 11 publications

References 10 publications

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

Investigation of lane-level GNSS positioning of vehicle in urban area

Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

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