融合颜色和深度信息的三维同步定位与地图构建研究
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摘要
摘要：同步定位与地图构建(Simultaneous Localization and Mapping, SLAM)是移动机器人实现未知环境中自主导航的基础,也是其实现自主化和智能化的前提条件之一。近年来,二维地图自主构建的理论与方法得到了深入研究并取得了丰富成果。随着传感器技术的进步和SLAM计算理论的不断发展,面向6自由度机器人的三维地图构建引起了研究者的关注。微软公司在2010年6月推出的廉价的RGB-D传感器——Kinect,为创建拥有丰富三维空间信息与颜色纹理信息的环境地图提供了新的可能。
本文针对室内未知环境下基于颜色信息与深度信息的三维同步定位和地图构建进行研究。在不需要任何先验知识的情况下,Kinect在室内场景中作6自由度运动并感知周围环境信息。同时提取稳定的环境特征点来表征3D空间实际物理点,以此作为路标来构建环境的三维几何地图。具体研究工作包括以下五个部分内容：
(1)对典型的深度摄像机Kinect在计算机视觉处理方面的应用进行了综述,针对Kinect的深度信息随着距离的增大出现显著畸变的问题,提出了一种无需人工干预的无监督学习的深度乘子图学习算法,从而达到深度校正的目的。该方法首先利用近距离测量的具有相对高精度的测量数据,采用常见的视觉测程+位姿图优化的RGB-D SLAM算法构建环境地图(须有闭环),然后利用该地图与深度测量数据的误差对深度乘子图进行学习,采用极大似然估计法逐步对其进行优化。与需要人工干预的方法不同,该方法可以在SLAM的过程中自动地完成深度校正的学习,便于用户使用。
(2)为了降低SLAM的复杂度和提高数据关联的可信度,对图像兴趣点的检测算法进行了深入研究。通过分析阈值t与层数o两个主要参数对OpenCV库中BRISK-AGAST检测算法性能的影响,提出了一种可调节的自适应特征检测方法——可调节的BRISK-AGAST检测器。该检测器的优点在于增强所提取的环境特征点的稳定性,提高SLAM过程中数据关联的几率和可信度,同时避免过多的环境特征在地图中表示,从而降低SLAM的复杂度。
(3)为了充分利用RGB-D图像的深度信息来更有效地区分环境特征点,对融合外观和深度信息的RGB-D图像特征描述符进行了研究,重点分析了BRAND描述符的机理。通过实验方法从运行时间、内存消耗,匹配性能等三个方面,将BRAND与EDVD、SURF、SIFT、 CSHOT、SPIN几种典型的特征描述算法作了比较,证明了它的优越性。
(4)为了克服目前基于图优化的RGB-D SLAM算法在缺少大的闭环约束情况下误差累积过大,不适用于在线应用的缺陷,提出了一种基于视觉航迹推算和扩展信息滤波的RGB-D SLAM方法,简称VO-EIF SLAM。利用相机针孔模型和基于高斯混合的深度不确定性度量模型,建立了RGB-D特征观测的三维不确定性模型,从而得到EIF SLAM的观测模型；设计了一种基于视觉残差的视觉航迹推算算法,用来估计运动控制输入信息；采用了二进制的特征描述BRAND来进行特征匹配,有效降低了数据关联的复杂度。同时,建立空间几何不确定性和二进制描述不确定性的统一模型,避免了显示地进行数据关联。
(5)深入研究了基于二进制描述符的快速特征关联算法,并将其应用于RGB-D SLAM快速闭环检测。分别设计和实现了二进制描述符的局部敏感哈希搜索算法和基于分层聚类树的快速二进制特征搜索算法来解决单个特征的快速关联问题；在此基础上,提出了一种融合局部几何约束的多特征点快速匹配算法,从而达到RGB-D SLAM快速闭环检测的目的。该算法利用了汉明距离来比较匹配度,有效提高了闭环检测的速度与精度。
最后,对全文进行了总结,并对今后进一步的研究方向进行了展望。文中共有图53幅、表3个、参考文献189篇。
Abstract:Simultaneous Localization and Mapping (SLAM) is the basis for mobile robot autonomous navigating in unknown environment, and also one of the prerequisites for realization of autonomous and intelligent. In recent years, the theories and methods of two-dimensional map building have been comprehensively studied and have achieved fruitful results. With the advances in sensor technology and the continuous development of SLAM computation theory, three-dimensional map building for the6-DOF robots has attracted researchers'attention. In June2010, Microsoft Corp launched a cheap RGB-D sensor named Kinect, which provides new possibilities for creating environment map with rich3D spatial information and color texture information.
This paper conducted simultaneous localization and map building based on RGB-D color information and depth information for indoor unknown environment. Without any prior knowledge, a Kinect does6-DOF motion in indoor scenes and perceive the surrounding environment information, extracting stable feature points of the environment to represent the actual physical point in3D space which is used as landmarks to create feature-based geometry map of the environment. The research work includes the following five parts:
(1) Reviewed the applications of the typical depth camera "Kinect" in computer vision processing. To resolve the significant depth distortion inherent in Kinect, which degree aggravated with increasing distance, an unsupervised learning algorithm without human intervention for depth multiplier image is proposed to achieve the purpose of depth correction. It first builds the environment map using a common visual odometry+pose graph optimization RGB-D SLAM algorithm from the relatively high accurate measurement data of short distance measurement (during the process loop-closing is needed). Then, the depth multiplier image is studied driven by the errors between the map and the depth measurement data, using the maximum likelihood estimation method gradually to optimize. Differ from the methods that require human intervention, this method can complete the learning of depth correction automatically during SLAM process, which makes it easier to use.
(2) In order to reduce the complexity of SLAM and increase the credibility of data association, interest points detection algorithm is deeply investigated. By analyzing the two parameters, threshold t and octave parameter o, impact on the performance of the BRISK-AGAST detection algorithm in OpenCV, an adjustable adaptive feature detection algorithm is proposed:adjustable-BRISK-AGAST detector. The new detector has the advantages that enhancing the stability of the extracted feature points, increasing the probability and reliability of data association in SLAM process, and avoiding excessive environmental features indicated in the map so as to reducing the complexity of SLAM.
(3) In order to take full advantage of RGB-D image depth information to more effectively distinguish between points of interest, the RGB-D image descriptors fusing appearance and geometric shape information are studied, focusing on the analysis of the mechanism of BRAND descriptor. Experimental results show that BRAND descriptor is superior to EDVD, SURF, SIFT, CSHOT, SPIN in processing time, memory consumption, matching performance.
(4) The current graph optimization based RGB-D SLAM algorithm is not suitable for online applications because in many cases the error accumulation will be very large due to absence of loop-closing. In order to overcome this defect, a new RGB-D SLAM method based on visual odometry and extended information filter, referred to as VO-EIF SLAM, is proposed. Using the pinhole camera model and the depth uncertainty measure model based on Gaussian mixture, a RGB-D features'three dimensional uncertainty measure model is established, which can be seen as the observation model of EIF SLAM. A visual dead reckoning algorithm based on visual residuals is devised, which is used to estimate motion control input. In addition, our observation model considers observations as sets of landmarks determined by their3D positions and their BRAND descriptors. We avoid explicit data association by marginalizing out the observation likelihood over all the possible associations, thus overcoming the problems derived from establishing incorrect correspondences between the observed landmarks and those in the map.
(5) Deeply studied a fast feature point association algorithm based on binary descriptors, and applied it to address the problem of loop-closing for RGB-D SLAM. Designed and implemented two fast binary features searching algorithms to solve the problem of fast data association for single feature point:the locality-sensitive hashing searching algorithm and hierarchical clustering based searching algorithm. Based on these, put forward a kind of multi feature point matching algorithm fusing local geometric constraints, thus achieve the purpose of quick close-loop detection for RGB-D SLAM. These algorithms use Hamming distance to compare matching degree and effectively improve the speed and accuracy of loop closure detection.
The conclusions and directions for future research work are discussed in the last chapter. There are53figures,3tables and189references.
本文针对室内未知环境下基于颜色信息与深度信息的三维同步定位和地图构建进行研究。在不需要任何先验知识的情况下,Kinect在室内场景中作6自由度运动并感知周围环境信息。同时提取稳定的环境特征点来表征3D空间实际物理点,以此作为路标来构建环境的三维几何地图。具体研究工作包括以下五个部分内容：
(1)对典型的深度摄像机Kinect在计算机视觉处理方面的应用进行了综述,针对Kinect的深度信息随着距离的增大出现显著畸变的问题,提出了一种无需人工干预的无监督学习的深度乘子图学习算法,从而达到深度校正的目的。该方法首先利用近距离测量的具有相对高精度的测量数据,采用常见的视觉测程+位姿图优化的RGB-D SLAM算法构建环境地图(须有闭环),然后利用该地图与深度测量数据的误差对深度乘子图进行学习,采用极大似然估计法逐步对其进行优化。与需要人工干预的方法不同,该方法可以在SLAM的过程中自动地完成深度校正的学习,便于用户使用。
(2)为了降低SLAM的复杂度和提高数据关联的可信度,对图像兴趣点的检测算法进行了深入研究。通过分析阈值t与层数o两个主要参数对OpenCV库中BRISK-AGAST检测算法性能的影响,提出了一种可调节的自适应特征检测方法——可调节的BRISK-AGAST检测器。该检测器的优点在于增强所提取的环境特征点的稳定性,提高SLAM过程中数据关联的几率和可信度,同时避免过多的环境特征在地图中表示,从而降低SLAM的复杂度。
(3)为了充分利用RGB-D图像的深度信息来更有效地区分环境特征点,对融合外观和深度信息的RGB-D图像特征描述符进行了研究,重点分析了BRAND描述符的机理。通过实验方法从运行时间、内存消耗,匹配性能等三个方面,将BRAND与EDVD、SURF、SIFT、 CSHOT、SPIN几种典型的特征描述算法作了比较,证明了它的优越性。
(4)为了克服目前基于图优化的RGB-D SLAM算法在缺少大的闭环约束情况下误差累积过大,不适用于在线应用的缺陷,提出了一种基于视觉航迹推算和扩展信息滤波的RGB-D SLAM方法,简称VO-EIF SLAM。利用相机针孔模型和基于高斯混合的深度不确定性度量模型,建立了RGB-D特征观测的三维不确定性模型,从而得到EIF SLAM的观测模型；设计了一种基于视觉残差的视觉航迹推算算法,用来估计运动控制输入信息；采用了二进制的特征描述BRAND来进行特征匹配,有效降低了数据关联的复杂度。同时,建立空间几何不确定性和二进制描述不确定性的统一模型,避免了显示地进行数据关联。
(5)深入研究了基于二进制描述符的快速特征关联算法,并将其应用于RGB-D SLAM快速闭环检测。分别设计和实现了二进制描述符的局部敏感哈希搜索算法和基于分层聚类树的快速二进制特征搜索算法来解决单个特征的快速关联问题；在此基础上,提出了一种融合局部几何约束的多特征点快速匹配算法,从而达到RGB-D SLAM快速闭环检测的目的。该算法利用了汉明距离来比较匹配度,有效提高了闭环检测的速度与精度。
最后,对全文进行了总结,并对今后进一步的研究方向进行了展望。文中共有图53幅、表3个、参考文献189篇。
Abstract:Simultaneous Localization and Mapping (SLAM) is the basis for mobile robot autonomous navigating in unknown environment, and also one of the prerequisites for realization of autonomous and intelligent. In recent years, the theories and methods of two-dimensional map building have been comprehensively studied and have achieved fruitful results. With the advances in sensor technology and the continuous development of SLAM computation theory, three-dimensional map building for the6-DOF robots has attracted researchers'attention. In June2010, Microsoft Corp launched a cheap RGB-D sensor named Kinect, which provides new possibilities for creating environment map with rich3D spatial information and color texture information.
This paper conducted simultaneous localization and map building based on RGB-D color information and depth information for indoor unknown environment. Without any prior knowledge, a Kinect does6-DOF motion in indoor scenes and perceive the surrounding environment information, extracting stable feature points of the environment to represent the actual physical point in3D space which is used as landmarks to create feature-based geometry map of the environment. The research work includes the following five parts:
(1) Reviewed the applications of the typical depth camera "Kinect" in computer vision processing. To resolve the significant depth distortion inherent in Kinect, which degree aggravated with increasing distance, an unsupervised learning algorithm without human intervention for depth multiplier image is proposed to achieve the purpose of depth correction. It first builds the environment map using a common visual odometry+pose graph optimization RGB-D SLAM algorithm from the relatively high accurate measurement data of short distance measurement (during the process loop-closing is needed). Then, the depth multiplier image is studied driven by the errors between the map and the depth measurement data, using the maximum likelihood estimation method gradually to optimize. Differ from the methods that require human intervention, this method can complete the learning of depth correction automatically during SLAM process, which makes it easier to use.
(2) In order to reduce the complexity of SLAM and increase the credibility of data association, interest points detection algorithm is deeply investigated. By analyzing the two parameters, threshold t and octave parameter o, impact on the performance of the BRISK-AGAST detection algorithm in OpenCV, an adjustable adaptive feature detection algorithm is proposed:adjustable-BRISK-AGAST detector. The new detector has the advantages that enhancing the stability of the extracted feature points, increasing the probability and reliability of data association in SLAM process, and avoiding excessive environmental features indicated in the map so as to reducing the complexity of SLAM.
(3) In order to take full advantage of RGB-D image depth information to more effectively distinguish between points of interest, the RGB-D image descriptors fusing appearance and geometric shape information are studied, focusing on the analysis of the mechanism of BRAND descriptor. Experimental results show that BRAND descriptor is superior to EDVD, SURF, SIFT, CSHOT, SPIN in processing time, memory consumption, matching performance.
(4) The current graph optimization based RGB-D SLAM algorithm is not suitable for online applications because in many cases the error accumulation will be very large due to absence of loop-closing. In order to overcome this defect, a new RGB-D SLAM method based on visual odometry and extended information filter, referred to as VO-EIF SLAM, is proposed. Using the pinhole camera model and the depth uncertainty measure model based on Gaussian mixture, a RGB-D features'three dimensional uncertainty measure model is established, which can be seen as the observation model of EIF SLAM. A visual dead reckoning algorithm based on visual residuals is devised, which is used to estimate motion control input. In addition, our observation model considers observations as sets of landmarks determined by their3D positions and their BRAND descriptors. We avoid explicit data association by marginalizing out the observation likelihood over all the possible associations, thus overcoming the problems derived from establishing incorrect correspondences between the observed landmarks and those in the map.
(5) Deeply studied a fast feature point association algorithm based on binary descriptors, and applied it to address the problem of loop-closing for RGB-D SLAM. Designed and implemented two fast binary features searching algorithms to solve the problem of fast data association for single feature point:the locality-sensitive hashing searching algorithm and hierarchical clustering based searching algorithm. Based on these, put forward a kind of multi feature point matching algorithm fusing local geometric constraints, thus achieve the purpose of quick close-loop detection for RGB-D SLAM. These algorithms use Hamming distance to compare matching degree and effectively improve the speed and accuracy of loop closure detection.
The conclusions and directions for future research work are discussed in the last chapter. There are53figures,3tables and189references.
引文
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