Bayesian localization for autonomous vehicle using sensor fusion and traffic signs

Verentsov S.I., Magerramov E.A., Vinogradov V.A., Gizatullin R.I., Alekseenko A.E., Kholodov Y.A.
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The localization of a vehicle is an important task in the field of intelligent transportation systems. It is well known that sensor fusion helps to create more robust and accurate systems for autonomous vehicles. Standard approaches, like extended Kalman Filter or Particle Filter, are inefficient in case of highly non-linear data or have high computational cost, which complicates using them in embedded systems. Significant increase of precision, especially in case when GPS (Global Positioning System) is unavailable, may be achieved by using landmarks with known location — such as traffic signs, traffic lights, or SLAM (Simultaneous Localization and Mapping) features. However, this approach may be inapplicable if a priori locations are unknown or not accurate enough. We suggest a new approach for refining coordinates of a vehicle by using landmarks, such as traffic signs. Core part of the suggested system is the Bayesian framework, which refines vehicle location using external data about the previous traffic signs detections, collected with crowdsourcing. This paper presents an approach that combines trajectories built using global coordinates from GPS and relative coordinates from Inertial Measurement Unit (IMU) to produce a vehicle's trajectory in an unknown environment. In addition, we collected a new dataset, including from smartphone GPS and IMU sensors, video feed from windshield camera, which were recorded during 4 car rides on the same route. Also, we collected precise location data from Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) device, which can be used for validation. This RTK-GNSS system was used to collect precise data about the traffic signs locations on the route as well. The results show that the Bayesian approach helps with the trajectory correction and gives better estimations with the increase of the amount of the prior information. The suggested method is efficient and requires, apart from the GPS/IMU measurements, only information about the vehicle locations during previous traffic signs detections.

Keywords: bayesian learning, sensor fusion, localization, autonomous vehicle
Citation in English: Verentsov S.I., Magerramov E.A., Vinogradov V.A., Gizatullin R.I., Alekseenko A.E., Kholodov Y.A. Bayesian localization for autonomous vehicle using sensor fusion and traffic signs // Computer Research and Modeling, 2018, vol. 10, no. 3, pp. 295-303
Citation in English: Verentsov S.I., Magerramov E.A., Vinogradov V.A., Gizatullin R.I., Alekseenko A.E., Kholodov Y.A. Bayesian localization for autonomous vehicle using sensor fusion and traffic signs // Computer Research and Modeling, 2018, vol. 10, no. 3, pp. 295-303
DOI: 10.20537/2076-7633-2018-10-3-295-303
URL: http://crm-en.ics.org.ru/journal/article/2684/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.
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