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A tightly coupled global navigation satellite system (GNSS)-Vision-IMU-wheel odometer (GVIWO) system is proposed, which can realize robust positioning in extreme environments. The purpose of this study is to achieve adaptive initialization in complex environments, sensor anomaly detection and processing, and adaptive robust localization in extreme environments.

Adaptive initialization includes traditional dynamic and static initialization and extreme condition initialization. To deal with the unstable visual features in the state of excited motion, a method of wheel odometer assisted initialization is designed. According to the abnormal condition of the sensor, the anomaly detection and attenuation mechanism are designed to realize the accurate positioning of the sensor under abnormal condition.

Tight coupling optimization of GNSS signals, RGB+Depth Map cameras, inertial measurement units and wheel odometers ensures accurate positioning in both indoor and outdoor environments. Through open data sets and field validation experiments, the proposed tightly coupled system has strong adaptability, especially in extreme environments.

A new framework is proposed by integrating GNSS, visual, inertial measurement unit (IMU) and wheel odometer sensors to form an efficient positioning solution. An adaptive initialization method is proposed to enhance the robustness and real-time performance of the positioning system in complex and dynamic environments. A mechanism for detecting and attenuating sensor anomalies is designed, enabling quasideterministic positioning under sensor anomalies.

Wheeled robots have been widely used in People’s Daily life. Accurate positioning is the premise of autonomous navigation. In this paper, an optimization-based visual-inertial-wheel odometer tightly coupled system is proposed, which solves the problem of failure of visual inertia initialization due to unobservable scale.The aim of this paper is to achieve robust localization of visually challenging scenes.

During system initialization, the wheel odometer measurement and visual-inertial odometry (VIO) fusion are initialized using maximum a posteriori (MAP). Aiming at the visual challenge scene, a fusion method of wheel odometer and inertial measurement unit (IMU) measurement is proposed, which can still be robust initialization in the scene without visual features. To solve the problem of low track accuracy caused by cumulative errors of VIO, the local and global positioning accuracy is improved by integrating wheel odometer data. The system is validated on a public data set.

The results show that our system performs well in visual challenge scenarios, can achieve robust initialization with high efficiency and improves the state estimation accuracy of wheeled robots.

To realize robust initialization of wheeled robot, wheel odometer measurement and vision-inertia fusion are initialized using MAP. Aiming at the visual challenge scene, a fusion method of wheel odometer and IMU measurement is proposed. To improve the accuracy of state estimation of wheeled robot, wheel encoder measurement and plane constraint information are added to local and global BA, so as to achieve refined scale estimation.