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First, is it possible to build map without landmarks for a robot in 2D? Let's say we have an aisle surrounded by two walls. The robot moves in this environment. Now is it feasible to build such a SLAM problem? Or landmarks must be available to do so?

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  • $\begingroup$ Do you really need both localisation and mapping ? The answer to your question depends on what sensors you're using. Like Josh said, do a bit more research on occupancy grid maps and feature based maps. Now, let us assume you've got a LRF module. Then the answer to the question "is it possible to build map without landmarks for a robot in 2D?" is 'Yes'. $\endgroup$ – metsburg Oct 30 '13 at 9:42
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I think you misunderstand what a landmark is. It is a generic, catch-all term for anything that a robot can recognize and use as part of a map. In particular, "landmarks" are important for feature-based SLAM algorithms, such as EKF-based slam. What you use for "landmarks" depends on what sensors are available to the robot.

In your case, since you haven't specified any sensing, then we'll assume the robot knows when it hits an object. Then any "landmark" is simply any time the robot bumps into something. If you do this with pen and paper, you'd just wander around and put an X any time you hit something, then turn and keep wandering. As time goes to infinity, you'd have a reasonable map of where object boundaries are, and what the object shapes are, as long as everything is static.

In this case, the "map" can just be a bitmap, where each pixel is 0 or 1, depending on if it has an object in that space or not. Scaling is up to the application.

I suggest doing a bit more research on these topics:

  • Occupancy Grid representation
  • Feature-based mapping
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  • $\begingroup$ thanks for being helpful and informative. I'm still confused though. Can I consider in the aforementioned example the walls as landmarks? I'm still confused because I've seen some videos in which there are some white circles which are recognized by the robot to determine its place. What the difference between the white circles and the walls? $\endgroup$ – CroCo Oct 29 '13 at 3:11
  • $\begingroup$ I don't know what videos you refer to. But The white circles are landmarks, and the walls are just walls with landmarks on them. Think about the word "marker" it doesn't mean anything particular, anything can be a "marker": a door handle marks a door, an X marks the spot, etc. Landmarks are just markers which are useful for mapping. They can be corners (which are easily recognized by lasers), or color blobs (which are easily recognized by cameras). The term landmark is flexible and determined by you the algorithm designer. $\endgroup$ – Josh Vander Hook Jan 31 '14 at 17:05
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I think it is necessary to define what a landmark actually is. The other answer just defines them as markers and gives some examples. Something more formal and distinctive would be the following:

Landmarks are features which can easily be re-observed and distinguished from the environment. They are used by the robot to find out where it is (to localize itself).

Landmarks should be easily re-observable. Individual landmarks should be distinguishable from each other. Landmarks should be plentiful in the environment. Landmarks should be stationary.

This would for example exclude a simple pixel to act as a landmark, since in the real world it corresponds to an object which will become more or less pixels depending on distance. A blob would be more suitable since we just rely on finding a closed shape of a certain color (again with some threshold because colors change depending on lightening conditions and angle). In the real world the blob would be some orange sticker attached to the wall for example.

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I just posted a similar question, trying to locate a paper than a co-worker remembered (ICRA? 2011? Not from Mudd nor the Foxe paper). I'd second @JustSomeHelp -- they need to be distinguishable. So a bump sensor alone won't help as you'll basically have (in naive form), one landmark that keeps getting different locations. That's going to end badly.

Clearly though, the recti-linear assumption is super helpful -- there's a 99 paper that address this. However I've not yet found a canonical approach to doing it

Z. J. Butler, A. A. Rizzi, and R. L. Hollis. Contact sensor-based coverage of rectilinear environments. In Proc. of IEEE Int’l Symposium on Intelligent Control, 1999.

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  • $\begingroup$ As I mentioned, this only works with a static environment. $\endgroup$ – Josh Vander Hook Jan 30 '15 at 18:47

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