Abstract
Precise, reliable, and low-cost vehicular localization across a continuous spatiotemporal domain is an important problem in the field of outdoor ground vehicles. This paper proposes a visual odometry algorithm, where an ultrarobust and fast feature-matching scheme is combined with an effective antiblurring frame selection strategy. Our method follows the procedure of finding feature correspondences from consecutive frames and minimizing their reprojection error. The blurred image is a great challenge for localization with a sharp turn or fast movement. So we attempt to mitigate the impact of blur with an image singular value decomposition antiblurring algorithm. Moreover, a statistic filter of feature space displacement and circle matching are proposed to screen or prune potential matching features, so as to remove the outliers caused by mismatching. An evaluation of benchmark dataset KITTI and real outdoor data, with blur, low texture, and illumination change, demonstrates that the proposed ego-motion scheme significantly achieved performance with respect to the other state-of-the-art visual odometry approaches to a certain extent.
Highlights
Precise, reliable, and low-cost vehicular localization across a continuous spatiotemporal domain of highway transportation is an important problem in the field of outdoor ground vehicles
This paper applies the video quality assessment index proposed by the Video Quality Experts Group (VQEG) to test the performance of different blurred degree assessment algorithms
(1) Outlier ratio (OR): OR = n1, n where n1 is the number of outlier images and n is the total number of images
Summary
Reliable, and low-cost vehicular localization across a continuous spatiotemporal domain of highway transportation is an important problem in the field of outdoor ground vehicles. Compared with the traditional videobased or magnetic coil-based detection method, we can obtain more refined real-time traffic parameters by the high-precision vehicle position With this information, we can calculate accurate micro and macrotraffic parameters (e.g., time headway, space headway, lane occupancy rate, average speed, traffic flow, traffic density, queue length, driving trajectory, congestion level, and accurate OD (origin-destination)). We can calculate accurate micro and macrotraffic parameters (e.g., time headway, space headway, lane occupancy rate, average speed, traffic flow, traffic density, queue length, driving trajectory, congestion level, and accurate OD (origin-destination)) These parameters provide powerful data support for advanced intelligent transportation applications (e.g., vehicle collision avoidance, parking induction, navigation, platoon collaborative control, and road network dynamic traffic allocation)
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