In this notebook, we look at implementing a 3D Voxel Map of our environment.

Now that we have gone over random sampling in a 2.5D environment, in this notebook we have what we need to construct a 3D, graph-based representation of the feasible parts of the configuration space.

In this notebook, we're going to discuss the Dubin's Car model and curved flight trajectories as a function of inertia.

In this notebook, we look into the concept of random sampling and how its implementation is effected by our 2.5D obstacle map.

In most lessons, thusfar, we've assumed an idealized version of the world and physics. We've assumed that the vehicle always knows where it is in the world as well as knowing where every obstacle is ahead of time. We've even assumed that the vehicle was able to follow a trajectory perfectly through the environment.

An advanced lane detection system visualized in a Jupyter Notebook

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