Simultaneous localization and mapping: Difference between revisions

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Sensor models divide broadly into landmark-based and raw-data approaches. Landmarks are uniquely identifiable objects in the world which ___location can be estimated by a sensor, such as [[Wi-Fi]] access points or radio beacons. Raw-data approaches make no assumption that landmarks can be identified, and instead model <math>P(o_t|x_t)</math> directly as a function of the ___location.

Optical sensors may be one-dimensional (single beam) or 2D- (sweeping) [[laser rangefinder]]s, 3D high definition light detection and ranging ([[lidar]]), 3D flash lidar, 2D or 3D [[sonar]] sensors, and one or more 2D [[camera]]s.<ref name="magnabosco13slam"/> Since the invention of local features, such as [[scale-invariant feature transform|SIFT]], there has been intense research into visual SLAM (VSLAM) using primarily visual (camera) sensors, because of the increasing ubiquity of cameras such as those in mobile devices. <ref name=Se2001>

{{cite conference

|last1=Se|first1=Stephen

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== History ==

AnA early demonstration of Simultaneous Localisation and mapping by a mobile robot appeared in theseminal work of James L. Crowley in 1985<ref>{{Cite journal |last=Crowley |first=J. |date=1985 |title=Navigation for an intelligent mobile robot |url=http://dx.doi.org/10.1109/jra.1985.1087002 |journal=IEEE Journal on Robotics and Automation |volume=1 |issue=1 |pages=31–41 |doi=10.1109/jra.1985.1087002 |issn=0882-4967}}</ref>. Theoretical foundations for a probabilistic solution to SLAM wereis presented inthe research of Randy C. Smith and Peter Cheeseman on the representation and estimation of spatial uncertainty in 1986.<ref name=Smith1986>{{cite journal

|last1=Smith|first1=R.C.

|last2=Cheeseman|first2=P.

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|archive-url=https://web.archive.org/web/20100702155505/http://www-robotics.usc.edu/~maja/teaching/cs584/papers/smith90stochastic.pdf

|archive-date=2010-07-02

}}</ref> TheOther acronym SLAM was first suggested in a panel discussion at the 1986 IEEE International Conference on Robotics and Automation (ICRA '86) in Raleigh North Carolina, that included James L. Crowley, Randy C. Smith, Peter Cheeseman, and Hugh Durrant-Whyte. Pioneeringpioneering work in this field was conducted by the research group of [[Hugh F. Durrant-Whyte]] in the early 1990s.<ref name=Leonard1991>{{cite book

|last1=Leonard|first1=J.J.

|last2=Durrant-whyte|first2=H.F.

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|isbn=978-0-7803-0067-5

|s2cid=206935019

}}</ref> which showed that solutions to SLAM exist in the infinite data limit. This finding motivates the search for algorithms which are computationally tractable and approximate the solution. The acronym SLAM was coined within the paper, "Localization of Autonomous Guided Vehicles" which first appeared in [[Information Systems Research|ISR]] in 1995.<ref>{{Cite journal|last1=Durrant-Whyte|first1=H.|last2=Bailey|first2=T.|date=June 2006|title=Simultaneous localization and mapping: part I|journal=IEEE Robotics & Automation Magazine|volume=13|issue=2|pages=99–110|doi=10.1109/MRA.2006.1638022|s2cid=8061430|issn=1558-223X|doi-access=free}}</ref>

The self-driving STANLEY and JUNIOR cars, led by [[Sebastian Thrun]], won the DARPA Grand Challenge and came second in the DARPA Urban Challenge in the 2000s, and included SLAM systems, bringing SLAM to worldwide attention. Mass-market SLAM implementations can now be found in consumer robot vacuum cleaners<ref>{{Cite news|last=Knight|first=Will|url=https://www.technologyreview.com/s/541326/the-roomba-now-sees-and-maps-a-home/|title=With a Roomba Capable of Navigation, iRobot Eyes Advanced Home Robots|work=MIT Technology Review|date=September 16, 2015|access-date=2018-04-25|language=en}}</ref> and [[Virtualvirtual reality headset|virtual reality headsets]]s such as the [[Meta Quest 2]] and [[PICO 4]] for markerless inside-out tracking.

== See also ==

{{Div col|small=yes}}

* [[Computational photography]]

* [[Kalman filter]]