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- W2904156610 abstract "Ego-motion estimation plays a crucial role in the achievement of autonomous driving and advanced driver assistance systems (ADAS). Classical approaches using wheel-based odometers suffers from errors due to the variation of wheels diameter, and are not robust to slippery conditions. This paper proposes an approach using static object detections from one or multiple low cost radars to improve the ego-motion estimation. First, the method determines a 2D motion of the ego vehicle for each radar available in the car. Three steps are applied: data association, linear regression, and outliers rejection. Then, the estimated 2D motions are fused with the proprioceptive sensors of the car in order to precisely estimate the speed and the yaw rate of the ego vehicle. In the tested scenarios, our approach drastically reduces the error speed with respect to classical wheel-based approach." @default.
- W2904156610 created "2018-12-22" @default.
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- W2904156610 date "2018-11-01" @default.
- W2904156610 modified "2023-09-26" @default.
- W2904156610 title "Ego-Motion Estimation with Static Object Detections from Low Cost Radars" @default.
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- W2904156610 doi "https://doi.org/10.1109/itsc.2018.8569738" @default.
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