Abstract
In this paper, an efficient and reliable framework to estimate the position and speed of moving vehicles is proposed. The method fuses LiDAR data with image based object detection algorithm output. LiDAR sensors deliver 3D point clouds with a positioning accuracy of up to two centimeters. 2D object data leads to a significant reduction of the search space. Outliers removal techniques are applied to the reduced 3D point cloud for a more reliable representation of the data. Furthermore, a multi-hypothesis Kalman filter is implemented to determine the target object’s speed. The accuracy of the position and velocity estimation is verified through real data and simulation. Additionally, the proposed framework is real-time capable and suitable for embedded-vision related applications.
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Dayangac, E., Baumann, F., Aulinas, J., Zobel, M. (2016). Target Position and Speed Estimation Using LiDAR. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_53
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DOI: https://doi.org/10.1007/978-3-319-41501-7_53
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