6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features

TL;DR

This paper presents an efficient 6D pose estimation method for complex, occluded, and symmetrical objects using an edge-focused point pair feature approach with a novel validation technique.

Contribution

It introduces a targeted down-sampling strategy and a pose hypothesis validation method to improve accuracy and efficiency in 6D pose estimation.

Findings

Outperforms existing methods on challenging datasets

Effectively handles symmetrical and occluded objects

Validated on real-world and simulated datasets

Abstract

The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis validation approach is proposed to resolve the symmetric ambiguity by calculating edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method on pose estimation of geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.