Tightly-coupled Monocular Visual-odometric SLAM using Wheels and a MEMS Gyroscope

TL;DR

This paper introduces a novel tightly-coupled visual-odometric SLAM system that integrates wheel and gyroscope data with monocular vision, enhancing accuracy and robustness for ground robot localization on a plane.

Contribution

It develops a new odometer preintegration theory on SO(3) and tightly integrates it into a visual SLAM framework, improving long-term localization robustness.

Findings

Demonstrates superior accuracy in experiments

Handles wheel slippage effectively

Provides robust tracking with incomplete measurements

Abstract

In this paper, we present a novel tightly-coupled probabilistic monocular visual-odometric Simultaneous Localization and Mapping algorithm using wheels and a MEMS gyroscope, which can provide accurate, robust and long-term localization for the ground robot moving on a plane. Firstly, we present an odometer preintegration theory that integrates the wheel encoder measurements and gyroscope measurements to a local frame. The preintegration theory properly addresses the manifold structure of the rotation group SO(3) and carefully deals with uncertainty propagation and bias correction. Then the novel odometer error term is formulated using the odometer preintegration model and it is tightly integrated into the visual optimization framework. Furthermore, we introduce a complete tracking framework to provide different strategies for motion tracking when (1) both measurements are available, (2) visual measurements are not available, and (3) wheel encoder experiences slippage, which leads the system to be accurate and robust. Finally, the proposed algorithm is evaluated by performing extensive experiments, the experimental results demonstrate the superiority of the proposed system.