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Open AccessArticle

Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

1
MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China
2
School of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China
3
School of Automotive Studies, Tongji University, 4800 Caoan Road, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(1), 161; https://doi.org/10.3390/s19010161
Received: 9 December 2018 / Revised: 28 December 2018 / Accepted: 31 December 2018 / Published: 4 January 2019
(This article belongs to the Special Issue Sensors Applications in Intelligent Vehicle)
Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving. View Full-Text
Keywords: autonomous driving; semantic landmark; parking lot; robust SLAM autonomous driving; semantic landmark; parking lot; robust SLAM
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MDPI and ACS Style

Zhao, J.; Huang, Y.; He, X.; Zhang, S.; Ye, C.; Feng, T.; Xiong, L. Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking. Sensors 2019, 19, 161.

AMA Style

Zhao J, Huang Y, He X, Zhang S, Ye C, Feng T, Xiong L. Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking. Sensors. 2019; 19(1):161.

Chicago/Turabian Style

Zhao, Junqiao; Huang, Yewei; He, Xudong; Zhang, Shaoming; Ye, Chen; Feng, Tiantian; Xiong, Lu. 2019. "Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking" Sensors 19, no. 1: 161.

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