SLAM

Simultaneous Localization And Mapping is an important field of research going on in the fields of AI and robotics.  It involves an autonomous machine being able to both map and navigate a completely unknown environment.  What really makes this difficult is the word simultaneous in SLAM.  It means the machine must be able to collect data, known where it is on its map, and create the map simultaneously.  Now unbeknownst to most, sensor technology; cameras, infrared distance finders, sonar, all have a flaw.  They have a terrible habit of being inaccurate when you need them most.  Now this by itself is not an issue for a human, a few cm's here, a few inches there.  The true issue comes when a machine is using these readings to decide its location in its environment.  The issues compound with every new incorrect input and eventually, the machine may think it is on the other side of its map, but is in fact in the room in started in.  For this very reason there are whole fields of SLAM dedicated to dealing with later realizing and attempting to correct errors in the machines map.  The final issue is every time the machine finds a new landmark, it must run SLAM's corrective algorithm on every landmark  it has seen in the past, combined with its new landmark.  This results SLAM developers having great difficulty making algorithms that don't force their robot to stop and compute for several hours before making its next move. The ability to allow their machines to move and map and the same time is in definition what SLAM strives for. Now there are several things that will make the demands of SLAM easier to accomplish with time.  The first is sensor accuracy.  With every year, our camera qualities get better and we develop improved measuring devices.  At the moment, SLAM developers can actually search and map in real time in a computer simulated environment.  In a way, SLAM has very little use in such perfect world scenarios were sensor inputs are exact.  The other avenue that will make the SLAM algorithm easier is our constantly improving processors.  However, with processor speeds increase slowing down, we may have to wait until the introduction of quantum processing to making SLAM resource use less taxing. Once SLAM has evolved, it will allow for a multitude of situations of be solved using autonomous robots.  Unmanned structural assessing of dangerous structures and automated search and rescue  through dangerous environments are two of my favorite theoretical uses for SLAM.



Contained here. on page 10 of this pdf is a map of a search done by a machine without SLAM's path correction run,  Next to it is a map with SLAM used.

Montemerlo, M., Thrun, S., Koller, D., & Wegbreit, B. (2002, July). FastSLAM: A factored solution to the simultaneous localization and mapping problem. In AAAI/IAAI (pp. 593-598).

R. Smith, M. Self, and P. Cheeseman. Estimating uncertain

spatial relationships in robotics. In

Autonomous Robot Vehicles, Springer,

1990.