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Article

Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving

by 1,2,†, 1,*,†, 1,3, 1, 1 and 1
1
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2
School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236037, China
3
School of Electrical and Electronic Engineering, Anhui Science and Technology University, Bengbu 233100, China
*
Author to whom correspondence should be addressed.
Co-first authors.
Academic Editor: Hemanth Venkateswara
Remote Sens. 2021, 13(9), 1828; https://doi.org/10.3390/rs13091828
Received: 10 February 2021 / Revised: 27 April 2021 / Accepted: 3 May 2021 / Published: 7 May 2021
(This article belongs to the Special Issue Computer Vision and Image Processing)
Motion estimation is crucial to predict where other traffic participants will be at a certain period of time, and accordingly plan the route of the ego-vehicle. This paper presents a novel approach to estimate the motion state by using region-level instance segmentation and extended Kalman filter (EKF). Motion estimation involves three stages of object detection, tracking and parameter estimate. We first use a region-level segmentation to accurately locate the object region for the latter two stages. The region-level segmentation combines color, temporal (optical flow), and spatial (depth) information as the basis for segmentation by using super-pixels and Conditional Random Field. The optical flow is then employed to track the feature points within the object area. In the stage of parameter estimate, we develop a relative motion model of the ego-vehicle and the object, and accordingly establish an EKF model for point tracking and parameter estimate. The EKF model integrates the ego-motion, optical flow, and disparity to generate optimized motion parameters. During tracking and parameter estimate, we apply edge point constraint and consistency constraint to eliminate outliers of tracking points so that the feature points used for tracking are ensured within the object body and the parameter estimates are refined by inner points. Experiments have been conducted on the KITTI dataset, and the results demonstrate that our method presents excellent performance and outperforms the other state-of-the-art methods either in object segmentation and parameter estimate. View Full-Text
Keywords: motion estimation; autonomous driving; region-level segmentation; extended Kalman filter motion estimation; autonomous driving; region-level segmentation; extended Kalman filter
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MDPI and ACS Style

Wei, H.; Huang, Y.; Hu, F.; Zhao, B.; Guo, Z.; Zhang, R. Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving. Remote Sens. 2021, 13, 1828. https://doi.org/10.3390/rs13091828

AMA Style

Wei H, Huang Y, Hu F, Zhao B, Guo Z, Zhang R. Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving. Remote Sensing. 2021; 13(9):1828. https://doi.org/10.3390/rs13091828

Chicago/Turabian Style

Wei, Hongjian, Yingping Huang, Fuzhi Hu, Baigan Zhao, Zhiyang Guo, and Rui Zhang. 2021. "Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving" Remote Sensing 13, no. 9: 1828. https://doi.org/10.3390/rs13091828

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