A Parameters optimization framework for pose estimation algorithm based on point cloud

Abstract

Pose estimation using point cloud data is pivotal in robotics. Despite the myriad of algorithms developed for this task, their efficiency is often contingent on optimal parameter settings. Parameters adjustment is a process that traditionally relies on extensive experience and deep understanding of the underlying algorithm. This manual, experience-driven process can hamper the adaptability and performance of pose estimation algorithms. Addressing this limitation, this paper proposes a novel framework for parameter optimization tailored to pose estimation algorithms. The framework introduces an objective function based on pose error, designed to enhance both the algorithm’s number of results and the accuracy. The methodology requires the object model and several scene point clouds with real poses of the objects.By synergistically integrating this objective function with various sampling and pruning algorithms to optimize the pose estimate algorithm, obtaining a set of superior parameters to replace default values becomes a straightforward process. Experimental assessments were conducted on the pose estimation algorithm implemented in Halcon, considering two types of objects within the Industrial 3D Object Detection Dataset (ITODD). The experimental results shown that the number and accuracy of matching results for optimized parameters are better than those proposed by algorithm developers. This not only underscores the framework’s potential as an alternative to conventional manual parameter tuning but also its utility as a foundational configuration for further refinements. Ultimately, the methodology augments the foundational efficiency and versatility of pose estimation algorithms, paving the way for more adept robotic interactions with a myriad of objects.