弱纹理黑暗场景下点线结合的红外惯性里程计  

A thermal-inertial odometry with point and line fusion for the weak textured dark scenes

作　　者：赖路广 赵冬青[1] 李林阳[1,2] 樊文哲 李雄庆 李鹏飞 LAI Luguang;ZHAO Dongqing;LI Linyang;FAN Wenzhe;LI Xiongqing;LI Pengfei(Institute of Surveying and Mapping,Information Engineering University,Zhengzhou 450001,China;State Key Laboratory of Geo-Information Engineering,Xi'an 710054,China)

机构地区：[1]信息工程大学地理空间信息学院,河南郑州450001 [2]地理信息工程国家重点试验室,陕西西安710054

出　　处：《测绘学报》2025年第3期448-460,共13页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(42474043,42104033);地理信息工程国家重点试验室开放基金(SKLGIE2023-Z-2-1);博士后科学基金(2022M712442)。

摘　　要：传统的视觉SLAM在光照不足或光照条件变化较大等恶劣环境中性能不佳甚至无法工作,而红外相机在黑暗、烟雾等挑战环境中具有更强的抗干扰能力。但红外相机噪声较大,成像质量较差,严重影响了红外SLAM的性能。本文基于红外相机热辐射成像的特点,顾及结构化场景存在的弱纹理特性,提出了一种点线结合的红外惯性里程计方法。在前端采用基于光流法的点特征跟踪算法,并对点特征进行筛选以剔除不稳定的点特征。同时对LSD算法进行改进以提取稳定的线特征,并基于LBD描述符进行线特征跟踪。在后端基于滑动窗口构建点、线、IMU信息的紧耦合图优化模型。最后,分别利用开源数据集和地下车库实测数据进行了验证。结果表明,点线结合的热惯性里程计的精度和稳健性较传统视觉SLAM算法有显著提升,有助于无人系统在黑暗、弱纹理的场景中实现稳健的定位。Traditional visual SLAM performs poorly or even fails to work in challenging environments such as significant changes in light conditions,darkness,smoke,and fog.In contrast,infrared cameras possess greater anti-interference capabilities.Nevertheless,the performance of infrared SLAM is severely affected by the poor imaging quality and noise of infrared cameras.In this paper,a point and line fusion infrared inertial odometry method is proposed,which is based on the thermal radiation imaging characteristics of infrared cameras and considers the weak texture characteristics in structured scenes.In the front-end,a point tracking algorithm based on the optical flow method is employed with a filtering process to eliminate unstable point features.The LSD algorithm is enhanced to extract stable line features,and line feature tracking is conducted using the LBD descriptor.A sliding window in the back-end is used to construct a tightly coupled graph optimization model that includes point,line,and IMU information.Finally,validation is conducted using open-source datasets and measured data from underground garages,respectively.The results demonstrate that the accuracy and robustness of the point-line combined thermal inertial odometer are significantly improved compared to traditional visual SLAM algorithms,which helps unmanned systems achieve robust localization in dark and weakly textured scenes.

关 键 词：SLAM 弱纹理环境 长波红外 点线结合 单目红外惯性里程计 

分 类 号：P232[天文地球—摄影测量与遥感]