Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry

TL;DR

This paper introduces a new co-planar parametrization method for stereo-SLAM and visual-inertial odometry that enhances accuracy and efficiency by leveraging geometric constraints and representing co-planar features effectively.

Contribution

It presents a novel parametrization scheme for co-planar points and lines that improves pose optimization in SLAM and VIO systems, validated through simulations and real-world datasets.

Findings

Outperforms traditional parametrizations in accuracy and efficiency

Reduces the size of the Hessian matrix in optimization

Demonstrates superior results on the EuRoC dataset

Abstract

This work proposes a novel SLAM framework for stereo and visual inertial odometry estimation. It builds an efficient and robust parametrization of co-planar points and lines which leverages specific geometric constraints to improve camera pose optimization in terms of both efficiency and accuracy. %reduce the size of the Hessian matrix in the optimization. The pipeline consists of extracting 2D points and lines, predicting planar regions and filtering the outliers via RANSAC. Our parametrization scheme then represents co-planar points and lines as their 2D image coordinates and parameters of planes. We demonstrate the effectiveness of the proposed method by comparing it to traditional parametrizations in a novel Monte-Carlo simulation set. Further, the whole stereo SLAM and VIO system is compared with state-of-the-art methods on the public real-world dataset EuRoC. Our method shows better results in terms of accuracy and efficiency than the state-of-the-art. The code is released at https://github.com/LiXin97/Co-Planar-Parametrization.