Quote:The tile size is determined by the wheel diameter. If you have small wheels, you have small tiles. If you have large wheels, you have large tiles. The current environment that I am testing in has tiles that are about 4.19 inches by 4.19 inches. This is because I have wheels that are 4 inches in diameter and if you take the wheel diameter * pi / 3, you come up with 4.188790266.... I round this to 2 decimal places. If you had wheels that were 2 inches in diameter, you would have tiles that are 2.09 inches. If you had wheels that were 12 inches in diameter, the tiles would be 12.57 inches. The logic is that the wheels would be much smaller for robots in smaller environments and much larger for robots in larger environments. Larger wheels means faster moving robots and thus the updating of the environment would have to account for faster moving robots. The number of tiles in the map is determined by the configuration screen by setting the size you want your map to be. In the test, the map is 50 feet x 50 feet. Using a robot with 12 inch diameter wheels indoors in a 50x50 foot house could become problematic. These are all subject to change depending on testing.
Well the information quoted above has changed. I am in the US and as such am more comfortable using inches and feet, so I am making 1 inch tiles for everything. The wheel diameter is still important but not as important in laying out the grid. I am converting the mm readings from the LIDAR to inches and marking the squares. We will see how this works out and go from there. This, along with everything else is subject to change as I go through it all.
The map on the screen is loaded from the SQLLite3 database initially. As things are seen by the LIDAR, the map table is updated and the display is updated by marking the corresponding tile on the map.
Eventually my goal is to take this logic and use it in SLAM. I plan on starting with some simple SLAM using the RANSAC algorithm which is best used in indoor environments. This is because it estimates and creates landmarks based on straight lines. From there I will use the Extended Kalman Filter for data association. This allows the robot to recognize landmarks and then adjust its current position on the map based on these landmarks.
One of the reasons that I want to store this information in a SQLLite3 database is that this would allow me to have multiple maps housed in different tables. The configuration screen could be modified to allow the user to specify which environment the robot is in (office 1, Office 2, home, Mom's house for example). These maps would be stored in different tables and the user would just switch to the map that pertains to the current environment. Another thing that these multiple maps could be used for is to handle different floors of an office building, one for each floor.
The test map is about 13 meg in size. This isn't too large but is only based on a 50x50 foot house on a robot with 4 inch diameter wheels. If you were in a warehouse or large office building with a robot with small wheels, the size of the database could get really large I would imagine. The goal is to get this to work in a smaller environment, and then see what needs to be done to handle larger environments.
Eventually, I plan on incorporating a path finding algorithm. This shouldn't be too hard to do because it is done in video games like crazy. There is plenty of sample code to build from.
Anyway, that is what I am working on currently. I suspect it will take some time before I have something to share. This is a pretty ambitious project and I will post updates as I accomplish different things with it.
I am not sure if I will sell this plugin or make it freely available. This is something that I will decide after I know how it works in multiple environments. If it turns out to be simply amazing, I might sell it. If it just works, I will give it away for free and continue working on a final solution.