I've written a grid based DFS algorithm with a PID-based steering system to maneuver a 30cm2 square-grid maze all in Python. The robot is a 4 wheel drive with an approximate size of 20 cm. The robot has a BeagleBone Green Wireless controller which is connected by USB to the RPLIDAR A1.
At this current moment, the robot is underutilizing the LIDAR and I want to begin to learn SLAM. However, the environment is highly predictable which I think makes a full SLAM counterintuitive. I would also like the code to be low CPU strain.
I've seen people converting a conventional SLAM into a grid based but only after the calculations are complete. I was wondering if there is a way to do a Grid Based SLAM right from the start (assume its position and map with a grid).
Accuracy isn't hugely important here as long as it understands a tile and the robot is able to avoid walls.
Any advice, tips or suggestion is appreciated. How would you store the map? How would you locate the position of the robot? How would you map the LIDAR's values?