基于特征的视觉同时定位与地图构建(Simultaneous Localization and Mapping,SLAM)存在实时性和鲁棒性差等问题,提出一种改进的基于四叉树的ORB特征提取方法,设计包含前后端及地图构建的机器人RGB-D SLAM算法。在前端使用四叉树方法完成...基于特征的视觉同时定位与地图构建(Simultaneous Localization and Mapping,SLAM)存在实时性和鲁棒性差等问题,提出一种改进的基于四叉树的ORB特征提取方法,设计包含前后端及地图构建的机器人RGB-D SLAM算法。在前端使用四叉树方法完成ORB特征的均匀提取,计算描述子间汉明距离实现特征匹配。根据随机采样一致性算法思想,结合EPNP(Efficient Perspective-N-Point)和迭代最近点法求解位姿,获取多次迭代后的准确位姿。采用关键帧进行回环检测,并且基于光速法平差优化位姿图,从而构建全局一致的3D地图,达到减少累积误差的目的。通过TUM数据集和多履带式全向移动机器人进行对比验证,实验结果满足实时性和稳定性要求,证明了算法的可行性和有效性。展开更多
针对目前视觉惯性SLAM(simultaneous localization and mapping)算法因缺少闭环检测而造成算法的准确性以及鲁棒性不高的问题,提出了一种适用于立体相机和单目相机的基于关键帧技术的视觉惯性SLAM算法。通过视觉惯性里程计提供局部连续...针对目前视觉惯性SLAM(simultaneous localization and mapping)算法因缺少闭环检测而造成算法的准确性以及鲁棒性不高的问题,提出了一种适用于立体相机和单目相机的基于关键帧技术的视觉惯性SLAM算法。通过视觉惯性里程计提供局部连续轨迹,通过非线性优化技术和闭环检测技术得到并行的全局连续轨迹,从而建立连续的全局地图。此外,算法具有在已获得的地图中进行重定位,并可以继续进行后续建图的能力。采用EuRoC数据集评价了算法的准确性、重定位能力以及运行时间。实验结果表明,与目前视觉惯性SLAM算法相比,该算法可以减少误差累积,减少漂移,重定位相机位置以及在已构建地图基础上继续构建地图。展开更多
针对PL-SLAM(Point and Line Simultaneous Localization And Mapping)算法在稠密场景下同时使用点线特征造成特征计算冗余,以及曲线运动时漏选关键帧等问题,提出一种基于改进关键帧选取策略的快速PL-SLAM算法(Improved keyframe extrac...针对PL-SLAM(Point and Line Simultaneous Localization And Mapping)算法在稠密场景下同时使用点线特征造成特征计算冗余,以及曲线运动时漏选关键帧等问题,提出一种基于改进关键帧选取策略的快速PL-SLAM算法(Improved keyframe extraction strategy-based Fast PL-SLAM algorithm,IFPL-SLAM).该算法引入一种基于信息熵引导的位姿跟踪决策,使用信息熵评价优先提取的特征点,依据评价结果决策点线特征的融合使用方式,避免了在纹理稠密场景下点线特征同时使用造成数据冗余,提高了算法的实时性;与此同时,为避免曲线运动时漏选关键帧,采用逆向索引关键帧选取策略补充在曲线运动中漏选的关键帧,提高了闭环的准确率和定位精度.在公开的KITTI数据集和TUM数据集中进行测试,测试结果表明本文算法的运行时间与PL-SLAM算法相比减少了16.0%,绝对轨迹误差相比于PL-SLAM算法缩小了23.4%,表现出了良好的构图能力.展开更多
Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract ...Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.展开更多
文摘针对目前视觉惯性SLAM(simultaneous localization and mapping)算法因缺少闭环检测而造成算法的准确性以及鲁棒性不高的问题,提出了一种适用于立体相机和单目相机的基于关键帧技术的视觉惯性SLAM算法。通过视觉惯性里程计提供局部连续轨迹,通过非线性优化技术和闭环检测技术得到并行的全局连续轨迹,从而建立连续的全局地图。此外,算法具有在已获得的地图中进行重定位,并可以继续进行后续建图的能力。采用EuRoC数据集评价了算法的准确性、重定位能力以及运行时间。实验结果表明,与目前视觉惯性SLAM算法相比,该算法可以减少误差累积,减少漂移,重定位相机位置以及在已构建地图基础上继续构建地图。
文摘针对PL-SLAM(Point and Line Simultaneous Localization And Mapping)算法在稠密场景下同时使用点线特征造成特征计算冗余,以及曲线运动时漏选关键帧等问题,提出一种基于改进关键帧选取策略的快速PL-SLAM算法(Improved keyframe extraction strategy-based Fast PL-SLAM algorithm,IFPL-SLAM).该算法引入一种基于信息熵引导的位姿跟踪决策,使用信息熵评价优先提取的特征点,依据评价结果决策点线特征的融合使用方式,避免了在纹理稠密场景下点线特征同时使用造成数据冗余,提高了算法的实时性;与此同时,为避免曲线运动时漏选关键帧,采用逆向索引关键帧选取策略补充在曲线运动中漏选的关键帧,提高了闭环的准确率和定位精度.在公开的KITTI数据集和TUM数据集中进行测试,测试结果表明本文算法的运行时间与PL-SLAM算法相比减少了16.0%,绝对轨迹误差相比于PL-SLAM算法缩小了23.4%,表现出了良好的构图能力.
基金The authors would like to thank Research Supporting Project Number(RSP2024R444)King Saud University,Riyadh,Saudi Arabia.
文摘Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.
