Joint estimation of position and attitude for intelligent vehicles by fusing delayed visual measurements |
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| Institution: | 1. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;2. School of Electrical Engineering, Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education (Anhui Polytechnic University), Wuhu 241000, China;3. State Key Laboratory of Synthetical Automation of Process Industries, Northeastern University, Shenyang, Liaoning 110819, China;1. College of Engineering, Peking University, Beijing 100871, PR China;2. Institute of Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China;1. School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China;2. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, China;3. School of Mathematics, Southeast University, Nanjing 210096, China;4. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;5. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, China |
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| Abstract: | Accurate position and attitude information is an important basis for normal driving of intelligent vehicles. In this paper, we investigate the estimation of position and attitude states for intelligent vehicles with low cost scheme. The low cost GNSS, camera, and proprioceptive sensors equipped by mass-produced vehicle are fused to estimate the states. The visual measurements adopted in this paper are based on the lateral distance and deflection angle to road features such as lane lines or curbs, which are generated more frequently than some other semantic features such as traffic lights, and leads to broader application scenario. Moreover, it is easier to implement compared with geometrical feature matching methods, since it only needs a simple prior map while latter needs large maps containing many high precision features. The visual measurements is often with large time delay due to negligible processing time. In order to fuse delayed measurements, a state-augmentation technique is adopted for the estimator design. The performance of the proposed method is evaluated by professional simulation software CarMaker, and shows that the incorporation of road features based visual measurement can effectively improve the position and attitude estimation accuracy by reducing the lateral position and yaw angle estimation error. |
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