In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camer...In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camera motion of two consecutive RGB-D frames by minimizing the photometric error.To permit outliers and noise,a robust sensor model built upon the t-distribution and an error function mixing depth and photometric errors are used to enhance the accuracy and robustness.Local graph optimization based on key frames is used to reduce the accumulative error and refine the local map.The loop closure detection method,which combines the appearance similarity method and spatial location constraints method,increases the speed of detection.Experimental results demonstrate that the proposed approach achieves higher accuracy on the motion estimation and environment reconstruction compared to the other state-of-the-art methods. Moreover,the proposed approach works in real-time on a laptop without a GPU,which makes it attractive for robots equipped with limited computational resources.展开更多
An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoo...An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.展开更多
针对传统ORB(Oriented FAST and Rotated BRIEF)算法提取图像特征时存在的特征点数量不足且分布不均匀问题,提出了一种基于四叉树的ORB特征阶梯分布算法。通过四叉树算法分割出特征点疏密不同的区域,对每个区域采用逐步降低阈值的方法,...针对传统ORB(Oriented FAST and Rotated BRIEF)算法提取图像特征时存在的特征点数量不足且分布不均匀问题,提出了一种基于四叉树的ORB特征阶梯分布算法。通过四叉树算法分割出特征点疏密不同的区域,对每个区域采用逐步降低阈值的方法,实现FAST(Features from Accelerated Segment Test)角点自适应提取;同时依据分割区域设置逐次递减的分割深度和特征点提取比例,以减少运算时间和特征冗余,使特征点分布更均匀。采用覆盖均匀度对特征点的均匀性进行量化。试验结果表明,该算法比传统ORB算法单幅图片的特征点提取数量平均多10.45%,覆盖均匀度平均低20%,运行时间比Mur-Artal算法平均减少20.54%,有效地提高了提取特征点的数量和均匀性,提升了运算效率。展开更多
vSLAM(visual Simultaneous Localization and Mapping)是一种基于视觉传感器实现同时定位与建图的技术,不仅可为地面机器人提供服务,同时在无人机的定位导航中也有着非常重要的应用。对基于无人机的vSLAM发展概况进行整理研究,就其中...vSLAM(visual Simultaneous Localization and Mapping)是一种基于视觉传感器实现同时定位与建图的技术,不仅可为地面机器人提供服务,同时在无人机的定位导航中也有着非常重要的应用。对基于无人机的vSLAM发展概况进行整理研究,就其中几大关键方向的研究现状予以介绍,主要包括结合IMU、结合光流传感器的vSLAM,同时总结目前研究中仍存在的一些问题和不足之处。结合经典理论与最新研究动态,对基于无人机的vSLAM重点研究内容和未来发展方向提出了新的展望。展开更多
基金Supported by the National Natural Science Foundation of China(61501034)
文摘In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camera motion of two consecutive RGB-D frames by minimizing the photometric error.To permit outliers and noise,a robust sensor model built upon the t-distribution and an error function mixing depth and photometric errors are used to enhance the accuracy and robustness.Local graph optimization based on key frames is used to reduce the accumulative error and refine the local map.The loop closure detection method,which combines the appearance similarity method and spatial location constraints method,increases the speed of detection.Experimental results demonstrate that the proposed approach achieves higher accuracy on the motion estimation and environment reconstruction compared to the other state-of-the-art methods. Moreover,the proposed approach works in real-time on a laptop without a GPU,which makes it attractive for robots equipped with limited computational resources.
文摘An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.
文摘针对传统ORB(Oriented FAST and Rotated BRIEF)算法提取图像特征时存在的特征点数量不足且分布不均匀问题,提出了一种基于四叉树的ORB特征阶梯分布算法。通过四叉树算法分割出特征点疏密不同的区域,对每个区域采用逐步降低阈值的方法,实现FAST(Features from Accelerated Segment Test)角点自适应提取;同时依据分割区域设置逐次递减的分割深度和特征点提取比例,以减少运算时间和特征冗余,使特征点分布更均匀。采用覆盖均匀度对特征点的均匀性进行量化。试验结果表明,该算法比传统ORB算法单幅图片的特征点提取数量平均多10.45%,覆盖均匀度平均低20%,运行时间比Mur-Artal算法平均减少20.54%,有效地提高了提取特征点的数量和均匀性,提升了运算效率。
文摘vSLAM(visual Simultaneous Localization and Mapping)是一种基于视觉传感器实现同时定位与建图的技术,不仅可为地面机器人提供服务,同时在无人机的定位导航中也有着非常重要的应用。对基于无人机的vSLAM发展概况进行整理研究,就其中几大关键方向的研究现状予以介绍,主要包括结合IMU、结合光流传感器的vSLAM,同时总结目前研究中仍存在的一些问题和不足之处。结合经典理论与最新研究动态,对基于无人机的vSLAM重点研究内容和未来发展方向提出了新的展望。
