Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements

TL;DR

This paper introduces an optimization-based visual-inertial SLAM method that is tightly integrated with raw GNSS data, jointly minimizing multiple error sources within a sliding window for improved accuracy in urban environments.

Contribution

It is the first to propose a tightly coupled optimization-based approach combining visual-inertial SLAM with raw GNSS measurements, addressing asynchronism and robustness issues.

Findings

Outperforms state-of-the-art visual-inertial SLAM in urban scenes.

Achieves better positioning accuracy than GNSS single point methods.

Handles challenging urban canyon environments effectively.

Abstract

Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge. More specifically, reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly minimized within a sliding window, in which the asynchronism between images and raw GNSS measurements is accounted for. In addition, issues such as marginalization, noisy measurements removal, as well as tackling vulnerable situations are also addressed. Experimental results on public dataset in complex urban scenes show that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, including scenes mainly containing low-rise buildings and those containing urban canyons.