Str-L Pose: Integrating Point and Structured Line for Relative Pose Estimation in Dual-Graph

TL;DR

This paper introduces a novel dual-graph neural network that combines point features and structured line segments to improve relative pose estimation accuracy in diverse environments, addressing limitations of point-only methods.

Contribution

The paper proposes a Geometric Correspondence Graph neural network that integrates point and line features with dual-graph modules for enhanced pose estimation.

Findings

Achieves competitive results on DeMoN and KITTI datasets.

Effectively exploits geometric constraints for better scene understanding.

Outperforms point-only methods in complex environments.

Abstract

Relative pose estimation is crucial for various computer vision applications, including Robotic and Autonomous Driving. Current methods primarily depend on selecting and matching feature points prone to incorrect matches, leading to poor performance. Consequently, relying solely on point-matching relationships for pose estimation is a huge challenge. To overcome these limitations, we propose a Geometric Correspondence Graph neural network that integrates point features with extra structured line segments. This integration of matched points and line segments further exploits the geometry constraints and enhances model performance across different environments. We employ the Dual-Graph module and Feature Weighted Fusion Module to aggregate geometric and visual features effectively, facilitating complex scene understanding. We demonstrate our approach through extensive experiments on the DeMoN and KITTI Odometry datasets. The results show that our method is competitive with state-of-the-art techniques.