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Loading...Introduction to Visual SLAM
Last quarter, our team discovered the importance of Visual Simultaneous Localization and Mapping (SLAM) in autonomous navigation systems. We were working on a project that required our robot to navigate through an unknown environment without any prior knowledge of the map. After trying various approaches, we finally settled on using OpenCV 5.6 and Cartographer 2.0 to implement Visual SLAM.
The Problem with Traditional SLAM Methods
Most traditional SLAM methods rely on lidar or other range sensors to build a map of the environment. However, these sensors can be expensive and may not always be available. Visual SLAM, on the other hand, uses only a camera to build a map and estimate the robot's pose. This makes it a more cost-effective and accessible solution for many applications.
How Visual SLAM Works
Visual SLAM works by tracking the movement of features in the environment and using this information to build a map. The process involves several steps: 1.
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