ALBERT

Description: With the rapid development of chip technology, low-cost Global Navigation Satellite System (GNSS) receivers nowadays support multi-constellation and dual-frequency observations, enabling wide usage of multi-GNSS precise point positioning (PPP). However, it is still difficult to provide reliable location information for multi-GNSS PPP in challenging environments because of insufficient satellite visibility. Thus, integrating other sensors to assist PPP is becoming a trend to achieve continuous high-precision positioning. Considering that the visual-inertial odometer (VIO) can provide more robust local pose estimation than the inertial navigation system, this study proposes a VIO-augmented multi-GNSS PPP navigation and positioning system named VIO-PPP. Multi-GNSS data is processed based on a dual-frequency undifferenced and uncombined model that can be easily extended to support multi-frequency GNSS observations. For achieving tightly coupled integration, this work employs factor graph optimization to fuse GNSS raw measurements with visual-inertial information. Corresponding factors are constructed for PPP, such as Doppler factor, pseudorange factor, carrier phase factor, ambiguity factor and receiver clock factor. It should be noted that the proposed VIO-PPP method does not need cycle slip detection because the float ambiguity of each epoch is estimated independently. Two field experiment datasets in different shadowed environments are used to test the proposed method and the results show that the VIO-PPP can significantly improve the positioning performance, especially when the direct GNSS signal is completely interrupted.