A GPS-aided Omnidirectional Visual-Inertial State Estimator in Ubiquitous Environments

The visual-inertial navigation system (VINS) has been a practical approach for state estimation in recent years. In this paper, we propose a general GPS-aided omnidirectional visual-inertial state estimator capable of operating in ubiquitous environments and platforms. Our system consists of two parts: 1) the pre-processing of omnidirectional cameras, IMU, and GPS measurements, and 2) the sliding window based nonlinear optimization for accurate state estimation. We test our system in different conditions including an indoor office, campus roads, and challenging open water surface. Experiment results demonstrate the high accuracy of our approach than state-of-the-art VINSs in all scenarios. The proposed odometry achieves drift ratio less than 0.5% in 1200 m length outdoors campus road in overexposure conditions and 0.65% in open water surface, without a loop closure, compared with a centimeter accuracy GPS reference.

I. Introduction

Accurate state estimation is a prerequisite in many robotic applications such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned surface vessels (USVs). Visual-inertial navigation system (VINS) estimators have led the trend in state estimation in the past decade with impressive progress by the community [1]–[3]. However, existing VINS estimators suffer from problems caused by operating environments and sensor configuration, limiting their usage in real-world robotic applications. For stabilizing perceptions, current VINS estimators are tested in restricted environments and with specific camera mechanical configuration [4]. In outdoor experiments, VINS estimators are facing challenges such as overexposure, featureless frames, and tiny pixel parallax for faraway features, resulting in the loss of stable features tracking. The configuration of cameras on UGVs or USVs, facing the front, strengthen these negative impacts. In open water environments near the coast for USVs, reliable visual measurements are only gained from nearby static objects from the shore. Current VINS approaches drift easily when the USV is making turns or when cameras are facing the sea surface.