M2C-GVIO:motion manifold constraint aided GNSS-visual-inertial odometry for ground vehicles  

作　　者：Tong Hua Ling Pei Tao Li Jie Yin Guoqing Liu Wenxian Yu 

机构地区：[1]Shanghai Key Laboratory of Navigation and Location Based Services,Shanghai Jiao Tong University,Shanghai,China

出　　处：《Satellite Navigation》2023年第1期77-91,I0003,共16页卫星导航（英文）

基　　金：the National Nature Science Foundation of China(NSFC)under Grant No.62273229;the Equipment PreResearch Field Foundation under Grant No.80913010303.

摘　　要：Visual-Inertial Odometry(VIO)has been developed from Simultaneous Localization and Mapping(SLAM)as a lowcost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning.However,VIOs usually have the degraded performance in challenging environments and degenerated motion scenarios.In this paper,we propose a ground vehicle-based VIO algorithm based on the Multi-State Constraint Kalman Filter(MSCKF)framework.Based on a unifed motion manifold assumption,we derive the measurement model of manifold constraints,including velocity,rotation,and translation constraints.Then we present a robust flter-based algorithm dedicated to ground vehicles,whose key is the real-time manifold noise estimation and adaptive measurement update.Besides,GNSS position measurements are loosely coupled into our approach,where the transformation between GNSS and VIO frame is optimized online.Finally,we theoretically analyze the system observability matrix and observability measures.Our algorithm is tested on both the simulation test and public datasets including Brno Urban dataset and Kaist Urban dataset.We compare the performance of our algorithm with classical VIO algorithms(MSCKF,VINS-Mono,R-VIO,ORB_SLAM3)and GVIO algorithms(GNSS-MSCKF,VINS-Fusion).The results demonstrate that our algorithm is more robust than other compared algorithms,showing a competitive position accuracy and computational efciency.

关 键 词：Sensor fusion Visual-inertial odometry Motion manifold constraint 

分 类 号：TN96[电子电信—信号与信息处理]