【摘要】 针对地下停车场环境GPS信号差、光线暗、特征少、纹理弱等带来的定位问题，提出了一种融合语义信息的视觉惯性定位算法.该算法首先通过视觉里程计和IMU预积分进行视觉惯性信息的融合.同时，利用4个鱼眼摄像头输入图像构建全景环视图像，并采用语义分割算法提取停车场环境语义信息.然后，根据视觉惯性紧耦合位姿完成逆投影变换，获得语义特征投影地图并采用回环检测和位姿图优化方式减小累积误差，完成全局位姿图优化，实现较高精度的定位效果.最后，通过Gazebo仿真与实车测试对该算法进行了验证.结果表明，本文算法能充分利用环境语义信息构建较为完整的语义地图，且基于重复定位误差对比，相较于ORB-SLAM3提高了车辆定位精度.更多还原

【Abstract】 A visual inertial localization algorithm integrating semantic information is proposed to address the positioning problems caused by poor GPS signals, dim lighting, limited features, and weak textures in underground parking lots. Firstly, this algorithm fuses visual inertial information through visual odometry and IMU preintegration. Simultaneously, a panoramic surround view image is constructed using four fisheye cameras, and semantic segmentation algorithms are employed to extract semantic information from the parking environment. Then, the semantic feature projection map is obtained through inverse projection transformation based on the tightly coupled visual inertial pose. Additionally, loop detection and pose graph optimization are employed to reduce accumulated errors and achieve global pose graph optimization, thereby achieving higher localization accuracy. This paper verifies the proposed algorithm through Gazebo simulation and real vehicle testing. The results indicate that this algorithm can fully utilize the semantic information of the environment to construct a complete semantic map and achieve higher vehicle localization accuracy than ORB-SLAM3 based on repeated localization error comparisons.更多还原