A Multi-Sensor Fusion Positioning Strategy for Intelligent Vehicles Using Global Pose Graph Optimization

Abstract

Positioning technology is an important component of environmental perception and the basis of decision-making and motion control for intelligent vehicles. Current independent positioning methods inevitably have defects and poor environmental adaptability. To improve positioning accuracy and stability using low-cost sensors, a multi-sensor fusion positioning strategy for intelligent vehicles using global pose graph optimization is proposed in this paper. This strategy complements the advantages of multiple sensors to obtain global optimal positioning results, and it can cope with a variety of challenging working conditions. Considering the fast response of the inertial measurement unit (IMU) and the advantages of a camera without drift, this strategy realizes the tightly coupled fusion of vision and the IMU to obtain the optimal pose of visual-inertial odometry (VIO) by nonlinear optimization. Moreover, this strategy refines the modeling of the IMU, and proposes a keyframe selection strategy to deal with driving in a straight line without obvious feature changes, turning, and other driving conditions. Taking into account the versatility of the global positioning system (GPS) and the low cost of the network real-time kinematic (NRTK) positioning, the output of VIO and GPS/NRTK is adaptively fused through global pose graph optimization to maintain global consistency. Finally, the multi-sensor fusion positioning strategy is verified using the KITTI dataset on the ROS simulation platform. The results show that the multi-sensor fusion positioning strategy for intelligent vehicles has better accuracy and environmental adaptability at a lower cost than state-of-the-art strategies.