摘要
目的基于视觉的同步定位与建图(visual-based simultaneous localization and mapping,vSLAM)是计算机视觉以及机器人领域中的关键技术,其通过对输入的图像进行处理分析来感知周围的3维环境以及进行自身的定位。现有的SLAM系统大多依赖静态世界假设,在真实环境中的动态物体会严重影响视觉SLAM系统的稳定运行。同时,场景中静止与运动部分往往和其语义有密切关系,因而可以借助场景中的语义信息来提升视觉SLAM系统在动态环境下的稳定性。为此,提出一种新的基于语义概率预测的面向动态场景的单目视觉SLAM算法。方法结合语义分割的结果以及鲁棒性估计算法,通过对分割进行数据关联、状态检测,从概率的角度来表示观测的静止/运动状态,剔除动态物体上的观测对相机位姿估计的干扰,同时借助运动概率及时剔除失效的地图点,使系统在复杂动态的场景中依然能够稳定运行。结果在本文构建的复杂动态场景数据集上,提出的方法在跟踪精度和完整度上都显著优于现有的单目视觉SLAM方法,而且在TUM-RGBD数据集中的多个高动态序列上也取得了更好的结果。此外,本文定性比较了动态场景下的建图质量以及AR(augmented reality)效果。结果表明,本文方法明显优于对比方法。结论本文通过结合语义分割信息以及鲁棒性估计算法,对分割区域进行数据关联以及运动状态检测,以概率的形式表示2D观测的运动状态,同时及时剔除失效地图点,使相机位姿估计的精度以及建图质量有了明显提升,有效提高了单目视觉SLAM在高度动态环境中运行的鲁棒性。
Objective Visual-based simultaneous localization and mapping(vSLAM)is essential for computer vision and robotic-related domain.The multiview and 3D structure scenarios can be recovered in terms of the input images analysis.Due to 3D objects in the real environment will seriously affect the stability of the vSLAM system,most of the existing vSLAM systems rely on static-scenarios assumption,which limits the application in the dynamic environment.Current geometry-based methods are focused on the negative effect alleviation of dynamic objects in checking some geometric constraints in 3D vision like epipolar constraints and re-projection error.Recent deep learning based semantic segmentation technology has been facilitating more effective information for SLAM system because the static and dynamic parts in the scene are often closely related to their semantics.Theoretically,due to the image information is transferred from pixel-level to semantic-level,the vSLAM system can be run stably in the dynamic environment in terms of the semantic information.However,some semantic-based SLAM schemes directly remove the semantic objects in the scene based on the segmentation results without considering their motion states.This may remove the areas that can provide stable visual features in some common real scenes,and the lack of sufficient observation will affect the stability of SLAM system severely.A feasible path is oriented to analyze the motion state of the semantic clustering,and applicability strategies are then implemented to alleviate the influence of the moving objects in visual localization module in vSLAM.The challenges of updating map in dynamic scene is required to be resolved as well.Method A new monocular vSLAM algorithm-based semantic probability prediction method is developed,which can combine semantic segmentation and robust estimation algorithm.To simplify training and generalization of network,the learning-based semantic segmentation module is opted.First,to track the certain cluster in 2D image space and keep
作者
潘小鹍
刘浩敏
方铭
王政
张涌
章国锋
Pan Xiaokun;Liu Haomin;Fang Ming;Wang Zheng;Zhang Yong;Zhang Guofeng(State Key Laboratory of CAD and CG,Zhejiang University,Hangzhou 310058,China;SenseTime Research,Beijing 100080,China;China Information Consulting and Designing Institute Co.,Ltd.,Beijing 100044,China)
出处
《中国图象图形学报》
CSCD
北大核心
2023年第7期2151-2166,共16页
Journal of Image and Graphics
基金
国家自然科学基金项目(61822310)。
关键词
视觉SLAM(vSLAM)
语义分割
动态场景
鲁棒性估计
概率预测
visual-based simultaneous localization and mapping(vSLAM)
semantic segmentation
dynamic environment
robust estimation
probability prediction
