To answer 2. in some more detail: We generally like to have GPS, cameras and lidar. Optionally, we add in IMU, radar and close in sensors (ie ultrasound).

We are approaching the ML problem somewhat differently, we try to give the robot an understanding of navigability of a space. This is done with semantically segmented images, overlayed with depth data where needed.

Once we have this, we flatten it into a 2d costmap of areas where kinematically (ie: ground clearance, terrain handling ability, allowable areas, etc) the vehicle is allowed to go. This is fed to our planner, which in turn generates valid paths for the vehicle to take.

The particular cameras and lidars used are abstracted away in a Hardware Abstraction Layer (HAL) that I've described a bit elsewhere on this page.

Cheers!