SLAM dunk

Once last brain dump before next week’s torture in Disneyland Paris: no, not crashing into inanimate objects; quite the opposite: Simultaneous Location And Mapping i.e. how to map obstacles’ location in space, attempting to avoid them initially through random choice of change in direction, mapping both the object location and the trial-and-error avoidance and in doing so, feeding backing into future less-randomized, more-informed direction changes i.e. a.i.

My plan here, as always, ignores everything described about standard SLAM processes elsewhere and does it my way based upon the tools and restrictions I have:

  • SLAM processing is carried out by the autopilot process.
  • GPS feeds it at 1Hz as per now.
  • Sweep feeds it every time a near obstacle is spotted within a few meters – perhaps 5?
  • The map is 0.5m x 0.5m resolution python dictionary indexed by integer units of 1,1 (i.e. twice the distance GPS measurement) into whose value is a score (resolution low due to GPS accuracy and Hermione’s physical size of 1m tip to tip)
  • GPS takeoff location = 0,0 on the map
  • During the flight, each GPS position is stored in the map location dictionary with a score of +100 points marking out successfully explored locations
  • Sweep object detection are also added to the dictionary, up to a limited distance of say 5m (to limit feed from Sweep process and ignore blockages too far away to matter).  These have a score of say -1 points due to multiple scans per second, and low res conversion of cm to 0.5m
  • Together these high and low points define clear areas passed through and identified obstructions respectively, with unexplored areas having zero value points in the dictionary.
  • Height and yaw are fixed throughout the flight to local Sweep and GPS orientation in sync.
  • The direction to travel within the map is the highest scoring next area not yet visited as defined by the map.

The above code and processing is very similar to the existing code processing the down facing video macro-blocks to guess the most likely direction moved; as such, it shouldn’t be too hard to prototype.  Initially the map is just dumped to file for viewing the plausibility of this method in an Excel 3D spreadsheet.

P.S. For the record, despite autonomous GPS testing being very limited, because the file-based flight plan works as well or better than the previous version, I’ve unloaded the latest code to GitHub.