Creating navigation map in semi-open scenarios for intelligent vehicle localization using multi-sensor fusion. (1st December 2021)

• Record Type: Journal Article
• Authors: Li, Yicheng; Cai, Yingfeng; Malekian, Reza; Wang, Hai; Sotelo, Miguel Angel; Li, Zhixiong
• Is Part Of: Expert systems with applications. Volume 184(2021)
• Publication Date: 2021-12-01
• Subjects: Intelligent vehicles -- Road scenario fingerprint -- Multi-view representation -- Multi-sensor fusion -- Map-based localization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115543
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A RSF map is proposed to collect the road features from multiple sensors. A new calibration method using plane-plane correspondences is developed. A new monocular 3D reconstruction method is introduced. Abstract: In order to pursue high-accuracy localization for intelligent vehicles (IVs) in semi-open scenarios, this study proposes a new map creation method based on multi-sensor fusion technique. In this new method, the road scenario fingerprint (RSF) was employed to fuse the visual features, three-dimensional (3D) data and trajectories in the multi-view and multi-sensor information fusion process. The visual features were collected in the front and downward views of the IVs; the 3D data were collected by the laser scanner and the downward camera and a homography method was proposed to reconstruct the monocular 3D data; the trajectories were computed from the 3D data in the downward view. Moreover, a new plane-corresponding calibration strategy was developed to ensure the fusion quality of sensory measurements of the camera and laser. In order to evaluate the proposed method, experimental tests were carried out in a 5 km semi-open ring route. A series of nodes were found to construct the RSF map. The experimental results demonstrate that the mean error of the nodes between the created and actual maps was 2.7 cm, the standard deviation of the nodes was 2.1 cm and the max error was 11.8 cm. The localization error of the IV was 10.8 cm. Hence, the proposed RSF map can be applied to semi-open scenarios in practice to provide a reliable basic for IV localization.