A tightly‐coupled compressed‐state constraint Kalman Filter for integrated visual‐inertial‐Global Navigation Satellite System navigation in GNSS‐Degraded environments

Abstract

Abstract Efficient multi‐sensor fusion is crucial to provide accurate pose estimates for navigating various next‐generation autonomous vehicles such as self‐driving cars, personal air vehicles, urban air mobilities, and electronic vertical take‐off and landing aircraft with respect to a unified global reference frame. The integration of the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) provides globally uniform coordinate information, and the Visual‐Inertial Odometry (VIO) gives stable and accurate local pose estimates. However, the error of the integrated INS/GNSS increases rapidly in GNSS‐challenged environments, and the VIO system suffers from the long‐term error accumulation. To cope with these difficulties, this paper proposes a new tightly coupled Visual‐Inertial‐GNSS (VIG) system to achieve robust and drift‐free pose estimation utilising GNSS raw measurements, image features, and inertial measurements. A novel compressed state constraint Kalman Filter (CSCKF) is formulated to combine time‐propagated feature measurements and differential GNSS observations to aid INS. Compared to the conventional integrated navigation systems, the proposed CSCKF‐based VIG system is advantageous in maintaining the minimum number of states without unnecessary state augmentation and no loss of performance. The proposed system is evaluated by field experiments in different GNSS availability situations. The results show that accuracy is improved significantly in GNSS‐degraded environments compared to that of the conventional systems.

Keywords

• compression filter
• multi‐GNSS
• multi‐sensor fusion
• visual‐inertial odometry