首页|An adaptive grid search algorithm for fitting spherical target of terrestrial LiDAR
An adaptive grid search algorithm for fitting spherical target of terrestrial LiDAR
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NSTL
Elsevier
? 2022 Elsevier LtdThe spherical target is critical in many terrestrial laser scanning applications. Accurately and robustly estimating its geometric center based on a point cloud is a problem of widespread concern. To address this problem, we propose a novel adaptive grid search (AGS) algorithm, which makes full use of the point cloud and geometric feature of the spherical target, and obtains the optimal fitting parameters through a finite number of iterative optimizations. Firstly, we utilize the centroid of the point cloud and the estimated radius of the spherical target as constraints to establish an initial constraint space. Secondly, we use grid search to determine the initial optimal solution of the parameters. Finally, we iteratively update the constraint space and the optimal solution utilizing the previous optimal solution and the parameters scales, and take the solution with the least error metric as the final optimal solution. We have validated the feasibility and reliability of the algorithm with actual data and simulated data, respectively, and compared the fitting results with those of the Non-Least Squares algorithm (NLS) and the M?estimator SAmple Consensus (MSAC) algorithm. The experimental results show that the AGS algorithm can achieve high-precision fitting of various point clouds of spherical targets and overcome the defects of the other two algorithms that are easily affected by noise and coverage. Due to iterative optimization, the running time of the AGS algorithm is slightly longer than the other two algorithms, but it can usually complete the fitting within 5 s.
NSTL
Elsevier
