复杂交通环境下基于关键目标的视觉SLAM  

Visual SLAM based on key targets in a complex traffic environment

作　　者：连静[1,2] 皮家豪 李琳辉[1,2] LIAN Jing;PI Jiahao;LI Linhui(School of Automotive Engineering,Dalian University of Technology,Dalian 116024,China;State Key Laboratory of Structural Analysis for Industrial Equipment,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学汽车工程学院,辽宁大连116024 [2]大连理工大学工业装备结构分析国家重点实验室,辽宁大连116024

出　　处：《重庆理工大学学报（自然科学）》2023年第1期19-25,共7页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(51775082,61976039,52172382);中央高校基本科研业务费专项基金项目(DUT20GJ207);大连市科技创新基金项目(2021JJ12GX015)。

摘　　要：为解决当前视觉SLAM(simultaneous localization and mapping,同时定位及地图构建)算法在近处纹理稀缺、动态物体遮挡等复杂交通环境下出现的定位失效的问题,提出一种基于关键目标的视觉SLAM算法。首先,以典型交通场景环境感知算法所检测的交通信号、标志等静止目标为基础,在静止目标中进行特征提取并筛选关键目标。其次,通过关键目标的类别和几何参数完成相连帧之间关键目标的匹配。然后,基于关键目标进行SLAM系统的初始化和跟踪,并通过最小化重投影误差求解当前相机位姿。最后,在局部建图线程中对相机位姿和关键目标三维坐标联合优化,并在局部地图中更新。经实验验证,所提算法能有效解决近处纹理缺失环境下的定位失效问题,保持了较高的定位精度,具有良好的环境适应性。Visual simultaneous localization and mapping(SLAM) plays an important role in autonomous driving. However, it is difficult for the current visual SLAM algorithm to solve those complex traffic scenes where few nearby textures but a large amount of dynamic object occlusion such as moving cars exists. Therefore, this paper proposes a visual SLAM algorithm based on key objects. The algorithm consists of five parts—key object extraction, system initialization, tracking based on key targets, pose solution and local bundle adjustment(BA). First of all, based on stationary targets detected by the environment perception algorithm in typical traffic scenes such as traffic signals and signs, this paper performs feature extraction in stationary targets and uses the quad tree to homogenize the feature points. According to the number of feature points, the key targets are screened and the process of key object extraction is finished. Secondly, the key target matching between the two continuous frames is performed in order to help system initialization. The matching criteria consist of the center, the height and the width of the key targets. System initialization is launched when the number of the matched key targets satisfies the need. RANSAC algorithm is used in feature point selection to compute the initial pose. A local map and map points are established at the end of the system initialization.Then, the constant motion model is applied to predict the center point of the key targets in the following frame. Besides the height and the width of the key targets, the number of the matched points is also taken into account to decide which key target in the current frame matches the corresponding one best.Furthermore, after data association is completed, the camera pose is estimated based on the feature points and the center point of each key target. Reprojection error is chosen to optimize the pose and distinguish the inliers and outliers in the process of optimization iteration. Lastly, local BA is utilized in local mapping thr

关 键 词：视觉SLAM 近处纹理稀缺 定位失效 关键目标 

分 类 号：U461.91[机械工程—车辆工程]