Talk:Simultaneous localization and mapping

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I tried

I tried to insert a link to Andrew Blake, pioneer of probabilistic methods for vision and tracking, but discovered there already is another Andrew Blake in the entertainment industry. Someone should resolve this ambiguity... Probots

Note: While A Blake has researched probabilistic methods and some SLAM methods use heavily a probabilistic approach, I consider unsuitable to force the issue here since there is no direct link between A Blake and SLAM. Baring differences is like having Kalman in the list because Kalman filters are used in some SLAM systems. —Preceding unsigned comment added by 137.222.103.132 (talk) 17:25, 3 June 2008 (UTC)[reply]

I like the intent of this sentence:

SLAM has not yet been fully perfected, but it is starting to be employed in unmanned aerial 
vehicles, autonomous underwater vehicles, planetary rovers and newly emerging domestic robots.

but have doubts as to what arial and underwater vehicles use SLAM for. I thought this technique was only for mapping indoor environments. -- BAxelrod 13:44, 15 October 2005 (UTC)[reply]

SLAM is a general tehnique/framework for creating maps dynamically while navigating in unknown territories. It is not domain-limited, and is therefore ideally suited for applications like autonomous arial and underwater navigation in unknown territories. Computational complexity may, however, pose a problem in such embedded devices. --Fredrik Orderud 23:35, 19 October 2005 (UTC)[reply]

Cleanup

Considering the importance of this (cursory google search find 20000+ citations), I think this article could definitely use some expert attention. Especially considering the shape it's in.--Anuran (talk) 00:01, 23 February 2013 (UTC)[reply]

I am going to contact someone who is at MIT, and a founder of the robotics project, to see if they can find anything out about how this [1] is progressing. SLAM has definitely become much more important over the last ten years, and this page does need some expansion really. Chaosdruid (talk) 15:42, 25 February 2013 (UTC)[reply]

New Section: "Solving the SLAM Problem" & Potential Future Edits

Echoing the concerns expressed above under "Cleanup," I have tried to improve this page (albeit incrementally) by (1) clarifying the introduction and (2) adding some much-needed (and well-sourced) information on relevant recent progress.

In my view, the rest of the page should be substantially revised, but I refrained from excising large sections of text because I feared that doing so would draw a moderator's ire.

To be completely frank, I don't think there is much content worth saving, but I am interested to hear what others think before making further changes.

I believe that this page could be great: SLAM is a fascinating example of a difficult problem in machine intelligence that we (humans) have managed to overcome and solve in the very recent past. In my view, that should be the focus of this page. Accordingly, I recommend the following approach to revising this page: — Explain what is SLAM is in a way that a lay reader understands and articulate why it was such a thorny problem — Explain how it is currently being solved (this is essentially what I have added today, but it could certainly be further improved) — Explain any shortcomings of the current attempts to solve the SLAM problem and discuss/review how researchers are working on overcoming enduring issues — Discuss the implications of the advances in solving the SLAM problem for other fields. Progress solving the problems presented by SLAM have a seemingly limitless potential for overcoming Moravec's paradox. Ideally, this page should recognize and explicitly flesh out that progress on the SLAM problem is a critical brick on the road to machines that are capable of understanding and thus manipulating the physical world in which they reside.

Feedback appreciated. — Preceding unsigned comment added by Ejkenkle (talk • contribs) 18:40, 15 July 2014 (UTC)[reply]