陀螺仪在基于Hash结构的三维重建中的应用
发布时间：2018-11-03 14:11
【摘要】：本文基于PrimeSense1.09深度相机研究室内场景的3D重建问题,对广泛研究和使用的传统KinectFusion算法存在的缺陷进行改进。研究了针对传统KinectFusion算法使用密集体积来存储3D场景信息,由于内存容量等的限制使得KinectFusion只能用于小场景物体的重建的不足,借鉴使用Hash结构的方法来解决内存空间限制这一问题。本文使用Hash结构,允许进行实时访问和隐面数据的更新,并且不需要定型或分层网格数据结构。而且,数据可以高效地进出Hash表,实现GPU和CPU数据交换,弥补空间不足的缺陷,这就为传感器运动时存储空间的可伸缩性提供了方便,并能够将新的场景信息不断拼接到已有的场景模型中,最终实现大范围场景信息的3D重建。研究了针对KinectFusion算法在位姿估计过程中使用点-面的最近点迭代(ICP)算法,由于其对初值敏感,且易陷入局部最小,在使用ICP算法进行匹配和跟踪定位时存在累积误差,导致相机突然运动时不能有效跟踪,在基于Hash结构的KinectFusion算法的基础上,提出一种结合陀螺仪进行扩展卡尔曼滤波器(EKF)数据融合的方法。使用一种型号为LPMS-CU(OEM)的陀螺仪传感器测量得到的相机位姿,与通过ICP算法计算得到的深度相机的位姿,使用EKF进行数据融合得到更加精确的位姿,并在相机突然运动时仍然能够实现有效跟踪,提高了算法的鲁棒性。实验结果表明,本文所采用的改进算法能在一定程度上解决KinectFusion算法存在的问题,能有效的应用于室内场景的3D重建中,并取得良好的效果。
[Abstract]:Based on the research of 3D reconstruction of indoor scene with PrimeSense1.09 depth camera, the defects of traditional KinectFusion algorithm, which is widely studied and used, are improved in this paper. Aiming at the traditional KinectFusion algorithm, we use dense volume to store 3D scene information. Because of the limitation of memory capacity, KinectFusion can only be used for the reconstruction of small scene objects. Learn from the use of Hash structure to solve the problem of memory space constraints. In this paper, the Hash structure is used to allow real-time access and update of hidden data, without the need for stereotyped or hierarchical grid data structures. Moreover, the data can enter and leave the Hash table efficiently, realize the data exchange between GPU and CPU, and make up the shortage of space, which provides convenience for the scalability of the storage space when the sensor moves. And the new scene information can be stitched into the existing scene model continuously, and finally the 3D reconstruction of large-scale scene information can be realized. In this paper, the nearest point iterative (ICP) algorithm is studied for KinectFusion algorithm in the process of position and attitude estimation. Because it is sensitive to initial value and easy to fall into local minimum, there are accumulated errors in matching and tracking location using ICP algorithm. As a result, the camera can not be tracked effectively when it moves suddenly. Based on the KinectFusion algorithm based on Hash structure, an extended Kalman filter (EKF) data fusion method based on gyroscope is proposed. Using a type of LPMS-CU (OEM) gyroscope sensor to measure the camera pose, and ICP algorithm to calculate the depth camera pose, using EKF data fusion to obtain a more accurate pose, It can also achieve effective tracking when the camera moves suddenly, and improve the robustness of the algorithm. The experimental results show that the improved algorithm can solve the problem of KinectFusion algorithm to some extent, and can be effectively applied to 3D reconstruction of indoor scene, and good results have been obtained.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
本文编号：2308024
[Abstract]:Based on the research of 3D reconstruction of indoor scene with PrimeSense1.09 depth camera, the defects of traditional KinectFusion algorithm, which is widely studied and used, are improved in this paper. Aiming at the traditional KinectFusion algorithm, we use dense volume to store 3D scene information. Because of the limitation of memory capacity, KinectFusion can only be used for the reconstruction of small scene objects. Learn from the use of Hash structure to solve the problem of memory space constraints. In this paper, the Hash structure is used to allow real-time access and update of hidden data, without the need for stereotyped or hierarchical grid data structures. Moreover, the data can enter and leave the Hash table efficiently, realize the data exchange between GPU and CPU, and make up the shortage of space, which provides convenience for the scalability of the storage space when the sensor moves. And the new scene information can be stitched into the existing scene model continuously, and finally the 3D reconstruction of large-scale scene information can be realized. In this paper, the nearest point iterative (ICP) algorithm is studied for KinectFusion algorithm in the process of position and attitude estimation. Because it is sensitive to initial value and easy to fall into local minimum, there are accumulated errors in matching and tracking location using ICP algorithm. As a result, the camera can not be tracked effectively when it moves suddenly. Based on the KinectFusion algorithm based on Hash structure, an extended Kalman filter (EKF) data fusion method based on gyroscope is proposed. Using a type of LPMS-CU (OEM) gyroscope sensor to measure the camera pose, and ICP algorithm to calculate the depth camera pose, using EKF data fusion to obtain a more accurate pose, It can also achieve effective tracking when the camera moves suddenly, and improve the robustness of the algorithm. The experimental results show that the improved algorithm can solve the problem of KinectFusion algorithm to some extent, and can be effectively applied to 3D reconstruction of indoor scene, and good results have been obtained.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
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