lxd19946的博客

opencv和opencv_contrib源码以及编译下载缺失的文件,可以参考我的博客进行编译

本代码是在海思3559A开发板上运行的,需要在ubuntu上运行的朋友,请自行修改cmakelists文件,实现了二维码的识别并将二维码切取保存,并输出二维码在原始图像的像素坐标.

在海思3559A上编译opencv+ffmpeg_zbar进行二维码识别测试,上面是源码包,交叉编译工具使用的是aarch64-himix100-linux-gcc,在ubuntu16.04实体系统上进行编译病运行.编译ffmpeg需要安装zlib和x264库,可自行去网上查找方法.

We present a real-time feature-based SLAM (Simultaneous Localization and Mapping) system for fisheye cameras featured by a large field-of-view (FoV). Large FoV cameras are beneficial for large-scale outdoor SLAM applications, because they increase visual overlap between consecutive frames and capture more pixels belonging to the static parts of the environment. However, current feature-based SLAM systems such as PTAM and ORB-SLAM limit their camera model to pinhole only. To compensate for the vacancy, we propose a novel SLAM system with the cubemap model that utilizes the full FoV without introducing distortion from the fisheye lens, which greatly benefits the feature matching pipeline. In the initialization and point triangulation stages, we adopt a unified vector-based representation to efficiently handle matches across multiple faces, and based on this representation we propose and analyze a novel inlier checking metric. In the optimization stage, we design and test a novel multi-pinhole reprojection error metric that outperforms other metrics by a large margin. We evaluate our system comprehensively on a public dataset as well as a self-collected dataset that contains real-world challenging sequences. The results suggest that our system is more robust and accurate than other feature-based fisheye SLAM approaches. The CubemapSLAM system has been released into the public domain.

很多的国际会议poster资源。在国际上发表论文的每一位学者都可以参考。

吴恩达的深度学习第一课第二周课后编程练习所需的材料