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3DLines-SLAM: A Monocular Vision Semi-Dense 3D Reconstruction Based on ORB-SLAM Abstract-Producing high-quality 3D maps and calculating more accurate camera pose has always been the goal of SLAM technology. The requirements of SLAM technology such as real-time, low computational cost, and low hardware cost are contradictory to the above objectives. For the issues listed above, we propose a novel semi-dense reconstruction algorithm based on the monocular ORB-SLAM system by matching the line segment features extracted from keyframes. Specifically, we build upon ORB-SLAM, the system first provides a set of keyframes and their corresponding camera poses and a series of map points in real-time. Then we use our developed a keyframe re-culling algorithm to culling redundant keyframes. Then an improved line segment extraction method is used to extract line segments in each keyframe. Finally, we use purely geometric constraints to generates accurate 3D scene model by matching 2D line segments from different keyframes. We thoroughly evaluate and in-depth analysis of our approach, the results show our system runs steadily and reliably. Not only the whole system has strong robustness, but also it can quickly generate an accurate 3d model online with low computational costs.
An open autonomous driving platform
Bundle Adjustment with CMakeLists.txt for ceres solver
基于opencv的摄像机标定 1 创建批量图片路径和列表 2 摄像机标定 3 图像批量去畸变 参考: https://blog.csdn.net/KYJL888/article/details/81043133
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A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
经纬高,高斯坐标6度带,3度带数据比较
Real-Time 3D Semantic Reconstruction from 2D data
Map data for China
Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities
Detailed comments for ORB-SLAM2 with trouble-shooting, key formula derivation, and diagrammatic drawing
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.