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Loading...Introduction to Autonomous Navigation Systems
Last quarter, our team discovered that building autonomous navigation systems requires a deep understanding of SLAM algorithms. We were working on a project that involved navigating a robot through a complex environment, and we needed a reliable and efficient way to map the surroundings. After trying out different approaches, we settled on using ROS 2, OpenCV 4.7, and NVIDIA Jetson Nano. In this article, I'll share our experience with building autonomous navigation systems using these technologies and provide a comparative study of SLAM algorithms using Cartographer and Orb-SLAM3.
Background on SLAM Algorithms
SLAM (Simultaneous Localization and Mapping) algorithms are a crucial component of autonomous navigation systems. They enable a robot to build a map of its environment while simultaneously localizing itself within that map. There are several SLAM algorithms available, each with its strengths and weaknesses. We chose to focus on Cartographer and Orb-SLAM3 because they are two of the most popular and widely-used algorithms in the field.
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