In general, lidar is used to remove the ambiguity in a local ground scan, and cameras extrapolate overlapping texture gradients to guess distant surface structure ( documented in the old book https://www.amazon.com/Learning-OpenCV-Computer-Vision-Libra... .)

There are some fairly good FOSS tools around like COLMAP, if you want to learn why automatic monocular pose recovery and SfM is hard.

Real autonomous robotics is hard, and people make the same predictable mistakes every 4 years. Retrofitting a consumer Yarbo would be cool though. =3

OpenCv is a great start, and is where I began.

this book: http://shop.oreilly.com/product/9780596516130.do has a number of worked examples that explain things well.

It does touch on Machine Learning, but it focuses much more on the fundamentals of computer vision, like feature detection, that allows things like SLAM to exist.

I have to say that this book: http://shop.oreilly.com/product/9780596516130.do

did a pretty good job of explaining everything for opencv proper.