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In this paper, an EKF (Extended Kalman Filter)-based algorithm is proposed to estimate 3D position and velocity components of different cars in a scene by fusing the semantic information and car model, extracted from successive frames with camera motion parameters. First, a 2D virtual image of the scene is made using a prior knowledge of the 3D Computer Aided Design (CAD) models of the detected cars and their predicted positions. Then, a discrepancy, i.e., distance, between the actual image and the virtual image is calculated. The 3D position and the velocity components are recursively estimated by minimizing the discrepancy using EKF. The experiments on the KiTTi dataset show a good performance of the proposed algorithm with a position estimation error up to 3–5% at 30 m and velocity estimation error up to 1 m/s.

remote sensing

Towards Semantic SLAM: 3D Position and Velocity Estimation by Fusing Image Semantic Information with Camera Motion Parameters for Traffic Scene Analysis