I have plenty of experience with software and a fair amount electronics. I’ve done Udacity’s Self Driving Car term 1 so I have a little machine learning and a decent amount of OpenCV experience. I’ve learned some about ROS.
I want a book that describes SLAM with an eye toward using the techniques, not deriving them mathematically. I want Kalman filters and Markov processes from a user’s perspective (ideally with pseudo code!). I want suggestions for doing motion control in a changing, imprecise environment using machine vision feedback. I want techniques to help me deal with crummy motors, maybe suggest some electronics and mechanical solutions for sluggishness (or extending their life). I want to understand motion planning, beyond what a little bit of ROS and Gazebo can give me. I understand inverse kinematics but occasionally get confused when I have multiple frames and need to move between coordinate frames (camera pixels, motor angles, real-world position). I know it is just a matrix multiply… except there are some big limitations to that (camera is x,y but real-world is x,y,z). How do people work with that? Are there strategies beyond adding camera(s)?
I have a lot more questions but what I’m trying to convey is that I want a book that beyond beginner. I wouldn’t mind learning more ROS but I’d rather use python and C/C++, simply because ROS packages drive me a bit crazy and smaller examples would be awesome.
It seems like most of the books I pick up are either for beginners using RPi / Arduino or graduate textbooks that are mostly math that I only barely follow then can only barely apply.
Suggestions? Thank you!