Back to Search
Start Over
Semantic Segmentation-Based Lane-Level Localization Using Around View Monitoring System
- Source :
- IEEE Sensors Journal. 19:10077-10086
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Lane-level localization is a fundamental task for autonomous driving. As front cameras are easily disturbed by dynamic objects in urban environments, this paper presents an accurate lane-level localization approach using the around view monitoring (AVM) system. The paper proposes to detect the road features (i.e., road boundaries and road markings) based on pixel-wise semantic segmentation of raw fisheye images. The method can detect various types of road features and exclude dynamic objects from the localization. To address the problem of AVM-based localization with road features of different characteristics, this paper proposes coarse-scale localization (CSL) and fine-scale localization (FSL) methods for high-accurate localization. The CSL method leverages the road boundaries to provide an initial position; the FSL method estimates a high-accuracy position by matching nearby road markings with the map. The experiments in urban environments demonstrate that the proposed approach achieves centimeter-level localization accuracy with five centimeters in the lateral direction and seventeen centimeters in the longitudinal direction.
- Subjects :
- Matching (graph theory)
Computer science
business.industry
010401 analytical chemistry
Feature extraction
Monitoring system
Image segmentation
01 natural sciences
0104 chemical sciences
Vehicle dynamics
Position (vector)
Segmentation
Computer vision
Artificial intelligence
Electrical and Electronic Engineering
business
Instrumentation
Subjects
Details
- ISSN :
- 23799153 and 1530437X
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Sensors Journal
- Accession number :
- edsair.doi...........403dc043bacdd716f01f6b83fc49dd4a