daniel-s-ingram/ai_for_robotics
Visualizations of algorithms covered in Sebastian Thrun's excellent Artificial Intelligence for Robotics course on Udacity.
This project offers clear visual demonstrations of fundamental algorithms for robot navigation and control. It takes in simulated robot movement and sensor data, then visually displays how algorithms like Histogram Localization, Kalman Filters, Particle Filters, and SLAM estimate a robot's position or map its environment. This is ideal for students, educators, or engineers learning or teaching the core concepts behind autonomous robotics.
161 stars. No commits in the last 6 months.
Use this if you need to understand or demonstrate how common robotics algorithms like localization, mapping, and path planning actually work through step-by-step visual examples.
Not ideal if you need a production-ready robotics library or a tool for real-world robot deployment.
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161
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33
Language
Python
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Last pushed
Jan 07, 2019
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