激光与光电子学进展, 2019, 56 (16): 161013, 网络出版: 2019-08-05   

一种改进的即时定位与地图构建系统 下载: 1129次

An Improved Simultaneous Localization and Mapping System
孙云雷 1,2,3,4,5,*吴清潇 1,2,4,5,**
作者单位
1 中国科学院沈阳自动化研究所, 辽宁 沈阳 110016
2 中国科学院机器人与智能制造创新研究院, 辽宁 沈阳 110016
3 中国科学院大学, 北京 100049
4 中国科学院光电信息处理重点实验室, 辽宁 沈阳 110016
5 辽宁省图像理解和计算机视觉重点实验室, 辽宁 沈阳110016
摘要
针对ORB-SLAM2 (Oriented FAST Rotated BRIEF SLAM2)系统中相机位姿求解精度不高,只能生成稀疏地图的问题,提出了一种在ORB-SLAM2系统框架上将稠密的直接法和原系统采用的稀疏特征法结合在一起求解相机位姿,并生成稠密地图的方法。该方法改进之处包括:在原系统使用的第三方图优化库g2o(General Graph Optimization)中创建一条新的稠密约束一元边,将稠密直接法的光度误差约束加入到图优化库g2o中;跟踪相机时先通过稠密直接法计算相邻两帧图像之间相机的旋转变换,再利用改进后的图优化库g2o同时最小化特征法的重投影误差和直接法的光度误差,优化求解6 DOF(Degree of Freedom)相机位姿;在ORB-SLAM2系统框架上添加稠密重建线程,将周围场景的重建结果实时反馈给用户。在TUM RGB-D和ICL-NUIM数据集上的测试结果表明,本文方法在一定程度上提高了ORB-SLAM2系统中相机位姿的求解精度,不仅可生成稀疏地图,还可重建更高精度的稠密地图。
Abstract
Camera pose estimation has a low accuracy and only generates a sparse map in the oriented FAST rotated BRIEF SLAM2 (ORB-SLAM2) system. To compute camera pose and generate a dense map, this study proposes a method that combines the dense direct method and sparse feature-based method adopted by the original ORB-SLAM2 system framework. This method mainly makes three improvements to the ORB-SLAM2 system. First, a new dense constraint unary edge is created in the third-party general graph optimization (g2o) library used in the original system; the photometric error constraint of the dense direct method is added to the g2o library. Second, the rotation transformation between two executive frames is calculated using the dense direct method; then, the improved g2o library is used to simultaneously minimize the re-projection error of the feature-based method and the photometric error of the direct method to compute the 6 degree-of-freedom (DOF) camera pose. Third, a dense reconstruction thread is added in the ORB-SLAM2 system framework and the reconstruction result of the surrounding scene is reported to the user in real time. Experiments conducted on TUM RGB-D and ICL-NUIM datasets reveal that the proposed method considerably improves the accuracy of the camera pose estimation in the ORB-SLAM2 system, produces sparse maps, and reconstructs high-precision dense maps.

孙云雷, 吴清潇. 一种改进的即时定位与地图构建系统[J]. 激光与光电子学进展, 2019, 56(16): 161013. Yunlei Sun, Qingxiao Wu. An Improved Simultaneous Localization and Mapping System[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161013.

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