Simulation of the RANSAC Algorithm to Determine Optimal Parameters
DOI:
https://doi.org/10.18372/1990-5548.89.21488Keywords:
RANSAC, geometric segmentation, LiDAR, autonomous navigation, parameter optimizationAbstract
The work is devoted to the study and search for optimal parameters of the RANSAC algorithm (distance threshold, number of points and iterations) for segmenting the ground surface in order to increase the reliability of autonomous navigation in complex conditions. The open multisensor HOPS dataset containing point clouds of garden landscapes with a high level of geometric noise was used for modeling. The algorithm was implemented in Python 3.10 using the Open3D library. It was found that using the minimum 3 required number of points to construct a plane is the most effective strategy to achieve the target accuracy. It was found that at low iteration numbers, the random nature of the point selection dominates the useful signal, causing significant instability and high errors. To evaluate the modeling results, two relative error metrics were used, which are based on a comparison of four types of points: true positive, true negative, false positive and false negative. Specific parameter configurations were identified that ensure the stability of the results even in the presence of a significant number of outliers in the input data. The work allowed us to determine the structure of the RANSAC parameter space, delimiting sensitive local optima and robust stable regions. The formulated conclusions create a methodological basis for narrowing the range of parameters in future experiments and increasing the autonomy of unmanned systems in complex unstructured environments.
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