An Efficient Method for Accurate Pose Estimation and Error Correction of Cuboidal Objects

2025-05-08

AI Summary

In stacked scenarios, high-precision autonomous grasping of cuboidal objects suffers from large pose estimation errors and computationally expensive, unstable local point cloud registration. To address these challenges, this paper proposes a novel linear-time pose error estimation and correction paradigm leveraging cubic structural priors. The method integrates global point cloud registration with lightweight local geometric constraints, enabling an analytical error compensation model that avoids the uncertainty and computational overhead inherent in iterative registration approaches. Experimental results demonstrate that the proposed method reduces both translational and rotational pose errors by over 40%, while maintaining inference latency consistently in the millisecond range. This significantly improves pose accuracy and real-time performance, meeting the stringent requirements of online robotic grasping in industrial settings.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The proposed system outlined in this paper is a solution to a use case that requires the autonomous picking of cuboidal objects from an organized or unorganized pile with high precision. This paper presents an efficient method for precise pose estimation of cuboid-shaped objects, which aims to reduce errors in target pose in a time-efficient manner. Typical pose estimation methods like global point cloud registrations are prone to minor pose errors for which local registration algorithms are generally used to improve pose accuracy. However, due to the execution time overhead and uncertainty in the error of the final achieved pose, an alternate, linear time approach is proposed for pose error estimation and correction. This paper presents an overview of the solution followed by a detailed description of individual modules of the proposed algorithm.

Problem

• Accurate pose estimation for cuboidal objects
• Error correction in autonomous object picking
• Efficient linear time pose error reduction

Innovation

• Efficient pose estimation for cuboidal objects
• Linear time error correction approach
• Combines global and local registration