Abstract
Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.
Highlights
Autonomous navigation technology has been used in various applications, such as agricultural robots, autonomous vehicles, and drones [1,2]
We used a horizontal delusion of precision (DOP) because it captures the influence of the satellite constellation on the position estimate in the horizontal plane, while ignoring the vertical component, given that the vehicle commonly drives in a horizontal plane
We proposed a practical method for constructing an odometry motion model and a global navigation satellite systems (GNSSs) sensor model
Summary
Autonomous navigation technology has been used in various applications, such as agricultural robots, autonomous vehicles, and drones [1,2]. Westbrook, and Subramanian [9] proposed a method for determining the observation noise using the number of satellites This method does not reflect individual non-systematic errors. This study proposes using three variables to represent the seven non-systematic errors by categorizing the errors based on their characteristics Of these three variables, two are determined via preliminary measurements, while the third variable is obtained by mapping according to the actual sensor error. Two are determined via preliminary measurements, while the third variable is obtained by mapping according to the actual sensor error This method can compute the observation noise independent of the performance of the sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated
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