Real-Time Pathing Risk Prediction

The code base for this project can be found here and here

For the final project of my Bayesian Robotics class, the only requirement was to come up with a practical implementation of some of the concepts that were taught in the class. As such, my partner and I had an idea to implement real-time risk mapping and tracking for autonomous robots using only a depth sensor similar to the one found on the Microsoft Kinect. Both static and moving objects would be detected, with the static objects having a fixed region of high risk surrounding the object that the robot will want to avoid. For moving objects, the movement of the object itself is tracked and predicted. Once the velocity and direction of the object is found, we can predict where the moving object is likely to be in the near future. This likelihood region maps directly to the areas where we would like the robot to avoid. To achieve this, we used the Asus Xtion Pro sensor along with OpenNI and OpenCV for data acquisition and processing. Qt was chosen as the cross-platform framework for the front-end GUI.

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