GNSS/Visual/IMU/Map Integration Via Sliding Window Factor Graph Optimization for Vehicular Positioning in Urban Areas

• Published in: IEEE transactions on intelligent vehicles pp. 1 - 11
• Main Authors: Bai, Xiwei, Hsu, Li-Ta
• Format: Journal Article
• Language: English
• Published: IEEE 12.06.2024
• Subjects: Accuracy; Atmospheric measurements; Factor graph optimization; Global navigation satellite system; GNSS; Map matching; Optimization; Particle measurements; Phase measurement; Urban areas; Visual-inertial navigation system; Visualization
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2024.3412208

Globally referenced and accurate positioning is of great significance for the application of intelligent vehicular systems. The visual and inertial measurement unit (IMU) integrated navigation system (VINS) is widely used in many intelligent positioning-based services. However, VINS are prone to drift over time and their performance is significantly compromised in urban canyons since the numerous outlier visual features caused by moving objects and unstable illuminations. Similarly, the global navigation satellite system (GNSS) is reliable in open areas but struggles with signal obstructions and reflections in urban canyons. To exploit the complementariness of the VINS and GNSS, this paper proposed a sliding window factor graph optimization (FGO)-based GNSS/Visual/IMU/Map (GVIM) integration, which exploits the full suit raw measurements from GNSS, IMU, and visual, together with the prior lightweight map. Unlike the existing tightly-coupled GNSS/Visual/IMU integration schemes, the potential of the carrier-phase measurement is fully explored using the recently developed window carrier phase (WCP) model. To further mitigate the impacts of the GNSS outliers, a novel sliding window (SW)-based map matching model is proposed to correct the states using the lightweight OpenStreetMap (OSM). Different from conventional filtering-based map matching, the states within the sliding window are associated with the lane information from the OSM which effectively exploits the measurement redundancy provided by the factor graph model. The experiments are conducted in the urban canyons of Hong Kong, utilizing a challenging dataset characterized by poor satellite visibility and numerous visual feature outliers, to verify the performance of the proposed method.