Aiming at the problem of system error and noise in simultaneous localization and mapping(SLAM) technology, we propose a calibration model based on Project Tango device and a loop closure detection algorithm based on v...Aiming at the problem of system error and noise in simultaneous localization and mapping(SLAM) technology, we propose a calibration model based on Project Tango device and a loop closure detection algorithm based on visual vocabulary with memory management. The graph optimization is also combined to achieve a running application. First, the color image and depth information of the environment are collected to establish the calibration model of system error and noise. Second, with constraint condition provided by loop closure detection algorithm, speed up robust feature is calculated and matched. Finally, the motion pose model is solved, and the optimal scene model is determined by graph optimization method. This method is compared with Open Constructor for reconstruction on several experimental scenarios. The results show the number of model's points and faces are larger than Open Constructor's, and the scanning time is less than Open Constructor's. The experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
室内三维场景实时感知是室内增强现实、三维测图和机器人自主定位与导航的关键技术之一。利用智能手机Tango传感器搭载的单目摄像头和深度相机,根据实时获取的彩色图像和深度信息,研究并提出了一种实时的室内三维场景感知方法;该方法首...室内三维场景实时感知是室内增强现实、三维测图和机器人自主定位与导航的关键技术之一。利用智能手机Tango传感器搭载的单目摄像头和深度相机,根据实时获取的彩色图像和深度信息,研究并提出了一种实时的室内三维场景感知方法;该方法首先根据智能手机Tango传感器获取的彩色图像存在的问题,采用多项式函数对彩色相机进行校正。接着,使用ORB(oriented FAST and rotated BRIEF)算子对彩色图像进行特征提取,利用RANSAC算法剔除误匹配点;并根据优化后的匹配点对采用ICP算法对手机姿态进行初步估计。最后,基于融合空间关系的视觉词典(SDBo W2)对感知的初步室内场景进行闭环检测;并通过图优化得到实时感知的室内三维场景模型。实验表明,方法能利用Tango传感器高效地获取室内场景完整的三维结构信息与纹理。展开更多
基金Supported by the National Natural Science Foundation of China(61772379)
文摘Aiming at the problem of system error and noise in simultaneous localization and mapping(SLAM) technology, we propose a calibration model based on Project Tango device and a loop closure detection algorithm based on visual vocabulary with memory management. The graph optimization is also combined to achieve a running application. First, the color image and depth information of the environment are collected to establish the calibration model of system error and noise. Second, with constraint condition provided by loop closure detection algorithm, speed up robust feature is calculated and matched. Finally, the motion pose model is solved, and the optimal scene model is determined by graph optimization method. This method is compared with Open Constructor for reconstruction on several experimental scenarios. The results show the number of model's points and faces are larger than Open Constructor's, and the scanning time is less than Open Constructor's. The experimental results show the feasibility and efficiency of the proposed algorithm.
文摘室内三维场景实时感知是室内增强现实、三维测图和机器人自主定位与导航的关键技术之一。利用智能手机Tango传感器搭载的单目摄像头和深度相机,根据实时获取的彩色图像和深度信息,研究并提出了一种实时的室内三维场景感知方法;该方法首先根据智能手机Tango传感器获取的彩色图像存在的问题,采用多项式函数对彩色相机进行校正。接着,使用ORB(oriented FAST and rotated BRIEF)算子对彩色图像进行特征提取,利用RANSAC算法剔除误匹配点;并根据优化后的匹配点对采用ICP算法对手机姿态进行初步估计。最后,基于融合空间关系的视觉词典(SDBo W2)对感知的初步室内场景进行闭环检测;并通过图优化得到实时感知的室内三维场景模型。实验表明,方法能利用Tango传感器高效地获取室内场景完整的三维结构信息与纹理。
