SP-VINS: A Hybrid Stereo Visual Inertial Navigation System based on Implicit Environmental Map

TL;DR

SP-VINS introduces a hybrid stereo visual inertial navigation system utilizing an implicit environmental map and a unified residual filter, enhancing long-term accuracy and efficiency in robot localization.

Contribution

It presents a novel filter-based stereo VINS with implicit environmental mapping, hybrid residual filtering, and online calibration, outperforming existing methods in accuracy and efficiency.

Findings

Achieves high computational efficiency in localization.

Maintains long-term high-accuracy state estimation.

Outperforms state-of-the-art methods in benchmarks.

Abstract

Filter-based visual inertial navigation system (VINS) has attracted mobile-robot researchers for the good balance between accuracy and efficiency, but its limited mapping quality hampers long-term high-accuracy state estimation. To this end, we first propose a novel filter-based stereo VINS, differing from traditional simultaneous localization and mapping (SLAM) systems based on 3D map, which performs efficient loop closure constraints with implicit environmental map composed of keyframes and 2D keypoints. Secondly, we proposed a hybrid residual filter framework that combines landmark reprojection and ray constraints to construct a unified Jacobian matrix for measurement updates. Finally, considering the degraded environment, we incorporated the camera-IMU extrinsic parameters into visual description to achieve online calibration. Benchmark experiments demonstrate that the proposed SP-VINS achieves high computational efficiency while maintaining long-term high-accuracy localization performance, and is superior to existing state-of-the-art (SOTA) methods.