## 1 Introduction  
Location awareness is one of the fundamental tasks in autonomous systems [1, 2, 3, 4]. Global Navigation Satellite System (GNSS) plays an irreplaceable role in global positioning. The differential carrier-phase-based GNSS positioning technique, referred to as Real-Time Kinematic (RTK), could achieve centimeter-level localization precision provided that the environment is open [5, 6]. Since the development of RTKLIB [7], even commercial-grade receivers are supported to generate fixed positioning solutions. However, essentially as a kind of Time of Arrival (TOA) based localization technique, GNSS is highly vulnerable to signal reflection and blockage, namely multi-path and None Line of Sight (NLOS) [8] receptions. According to our recent open-sourced UrbanNav dataset in [9], the positioning error of the GNSS-RTK can easily be larger than 10 m in typical urban scenarios of Hong Kong via a commercial level u-box M8T GNSS receiver. In other words, the GNSS-RTK is significant for providing globally referenced positioning but the accuracy is not guaranteed in urban canyons, with a strong reliance on the degree of urbanization [10]. Intuitively, to enhance the performance of GNSS-RTK standalone-based positioning, multi-sensor fusion is a promising approach with the complementary properties of heterogeneous sensors taken into consideration [11, 12, 13]. The integration of GNSS and InertialMeasurement Unit (IMU) is extensively investigated. To facilitate the application in the metropolis, the key point is to mitigate the degradation caused by abnormal GNSS solutions and constrain the IMU bias during GNSS outage timely in the integration [14; 15; 16]. The work in [17], [PERSON], [PERSON] proposed a loosely coupled method implemented by an adaptive Kalman Filter (KF) that adjusts the GNSS measurement noise covariance according to parameters output by the receiver such as the Position Dilution of Precision (PDOP), numbers of tracked satellites, etc. However, the indicator inferred from the PDOP and the number of tracked satellites are far from enough for GNSS quality identification in complex urban scenarios. The GNSS positioning error is classified via random forest training in [18] and further smoothed by a fuzzification interface in [19]. Yet the method relied heavily on the selection of the features for machine learning. The work conducted by [PERSON], [PERSON], etc. [20] adopted the Rauch-Tung-Striebel (RTS) smoother [21] for the tight fusion of GNSS and IMU which is only suitable for the postprocessing manner. The work of [PERSON], [PERSON] [22] presented the tightly coupled integration of single-frequency multi-GNSS RTK and low-cost IMU. Outlier-resistant Ambiguity Resolution (AR) and KF contribute to the improvement of the fixing rate by 10%. Nevertheless, the residual-based outlier rejection easily caused false-negative judgment. In the work of [PERSON], [PERSON] [23] an Auto-Regressive Integrated Moving Average (ARIMA) model is innovatively leveraged to predict the GNSS measurements when it goes offline in city canyons before the loosely coupled Factor Graph Optimization (FGO) [24] with IMU. The work in [25], [PERSON], [PERSON] enhanced the AR performance of the GNSS after the inclusion of a priori position from IMU based on Extended KF (EKF). Unfortunately, all those existing GNSS/IMU integration schemes relied on the technical grade of the IMU when the GNSS solution is poor. Light Detection and Ranging (LiDAR) gains increasing attention nowadays in both autonomous systems related to academic research and industrial manufacture [26; 27; 28; 29]. Its high precision and long-range detection are immune to illumination [30]. The integration of LiDAR and IMU is a popular way for 6D motion estimation and map building, namely LiDAR/IMU odometry (LIO) [31; 32; 33], during which the bias of IMU is corrected by LiDAR while the motion distortion of LiDAR measurements is removed by IMU. Nonetheless, the results produced by these local sensors are relative and thus suffer from accumulated drift.