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영어

The calibration between a camera and a two-dimensional laser scanner (2DLS) is an essential step in the object detecting system. Many algorithms with linear model have been proposed. But these tend to solve intrinsic and extrinsic calibration parameters separately and are influenced seriously by the poor initial data, which leads to unstable and inaccurate results. Hence, a new nonlinear model based on the Back Propagation neural network trained by the Levenberg-Marquardt algorithm (LM-BP) is presented for calibration in this paper. Before the calibration, the original laser data is fitted linearly to avoid the ranging error and is optimized by an angular increment to reduce the step-angular error. Then, the calibration network with 4 inputs composed of the lasers points’ coordinates and constant 1, and 2 outputs are obtained, expected values of which are the coordinates of corresponding points in the image coordinates. The sum of square of errors between the network outputs and expected values is taken to adjust the modifications of the weights and thresholds with the Levenberg-Marquardt method to optimize the calibration model. Finally, compared with related researches, experimental results show that the accuracy of calibration between camera and 2DLS is significantly improved, and the detecting system is more suitable for actual measurement situations.