A global navigation satellite system (GNSS) is a sensor that can acquire 3D position and velocity in an earth-fixed coordinate system and is widely used for outdoor position estimation of robots and vehicles. Various GNSS/inertial measurement unit (IMU) integration methods have been proposed to improve the accuracy and availability of GNSS positioning. However, all of them require the addition of a 3D attitude to the estimated state in order to fuse the IMU data. This study proposes a new optimization-based positioning method for combining GNSS and IMU that does not require attitude estimation. The proposed method uses two types of constraints: one is a constraint between states using only the magnitude of the 3D acceleration observed by an accelerometer, and the other is a constraint on the angle between the velocity vectors using the amount of angular change by a gyroscope. The evaluation results with simulation data show that the proposed method maintains the position estimation accuracy even when the IMU mounting position error increases and improves the accuracy when the GNSS observations contain multipath errors or missing data. The proposed method could improve the positioning accuracy in experiments using IMUs acquired in real environments.

翻译：全球导航卫星系统（GNSS）是一种能够在地球固定坐标系中获取三维位置和速度的传感器，广泛应用于机器人和车辆室外位置估计。为提高GNSS定位的精度与可靠性，目前已提出多种GNSS/惯性测量单元（IMU）集成方法。然而，这些方法均需在估计状态中增加三维姿态信息以融合IMU数据。本研究提出一种无需姿态估计的优化定位方法，用于融合GNSS与IMU数据。该方法采用两种约束：一是利用加速度计观测的三维加速度幅值建立状态间约束，二是通过陀螺仪角速度变化量约束速度矢量间的夹角。基于仿真数据的评估结果表明，当IMU安装位置误差增大时，该方法仍能保持位置估计精度；当GNSS观测存在多路径误差或数据缺失时，该方法可提升定位精度。在实际环境中获取的IMU实验验证中，所提方法有效改善了定位性能。