Advancements in Translation Accuracy for Stereo Visual-Inertial Initialization

TL;DR

This paper introduces a novel initialization method for stereo visual-inertial SLAM that improves translation accuracy by using a 3 DoF bundle adjustment and better rotation estimation, outperforming existing methods on benchmark datasets.

Contribution

It proposes a new initialization approach that independently optimizes translation with 3 DoF BA and refines rotation using IMU data, enhancing accuracy over prior methods.

Findings

Outperforms existing initialization methods on EuRoC dataset

Achieves more accurate translation estimates in challenging scenarios

Demonstrates robustness and improved accuracy in SLAM initialization

Abstract

As the current initialization method in the state-of-the-art Stereo Visual-Inertial SLAM framework, ORB-SLAM3 has limitations. Its success depends on the performance of the pure stereo SLAM system and is based on the underlying assumption that pure visual SLAM can accurately estimate the camera trajectory, which is essential for inertial parameter estimation. Meanwhile, the further improved initialization method for ORB-SLAM3, known as Stereo-NEC, is time-consuming due to applying keypoint tracking to estimate gyroscope bias with normal epipolar constraints. To address the limitations of previous methods, this paper proposes a method aimed at enhancing translation accuracy during the initialization stage. The fundamental concept of our method is to improve the translation estimate with a 3 Degree-of-Freedom (DoF) Bundle Adjustment (BA), independently, while the rotation estimate is fixed, instead of using ORB-SLAM3's 6-DoF BA. Additionally, the rotation estimate will be updated by considering IMU measurements and gyroscope bias, unlike ORB-SLAM3's rotation, which is directly obtained from stereo visual odometry and may yield inferior results when operating in challenging scenarios. We also conduct extensive evaluations on the public benchmark, the EuRoC dataset, demonstrating that our method excels in accuracy.