They provide a set which encloses the pose of the robot and a set approximation of the map. [[Bundle adjustment]], and more generally [[Maximum a posteriori estimation]] (MAP), is another popular technique for SLAM using image data, which jointly estimates poses and landmark positions, increasing map fidelity, and is used in commercialized SLAM systems such as Google's [https://developers.google.com/ar/ ARCore] which replaces their previous [[augmented reality]] project '[[Project Tango|Tango]]'. MAP estimators compute the most likely explanation of the robot poses and the map given the sensor data, rather than trying to estimate the entire posterior probability.
New SLAM algorithms remain an active research area,<ref name=":0">{{Cite journal|last1=Cadena|first1=Cesar|last2=Carlone|first2=Luca|last3=Carrillo|first3=Henry|last4=Latif|first4=Yasir|last5=Scaramuzza|first5=Davide|last6=Neira|first6=Jose|last7=Reid|first7=Ian|last8=Leonard|first8=John J.|date=2016|title=Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age|journal=IEEE Transactions on Robotics|language=en-US|volume=32|issue=6|pages=1309–1332|arxiv=1606.05830|bibcode=2016arXiv160605830C|doi=10.1109/tro.2016.2624754|issn=1552-3098|hdl=2440/107554|s2cid=2596787}}</ref> and are often driven by differing requirements and assumptions about the types of maps, sensors and models as detailed below. Many SLAM systems can be viewed as combinations of choices from each of these aspects.
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| conference = Int. Conf. on Robotics and Automation (ICRA)
| doi = 10.1109/ROBOT.2005.1570091
}}</ref> Visual and [[Lidar|LIDAR]] sensors are informative enough to allow for landmark extraction in many cases. Other recent forms of SLAM include tactile SLAM<ref>{{cite conference | last1= Fox |first1=C. |last2=Evans |first2=M. |last3=Pearson |first3=M. |last4=Prescott |first4=T. | title = Tactile SLAM with a biomimetic whiskered robot. | conference = Proc. IEEE Int. Conf. on Robotics and Automation (ICRA) | year = 2012|url=http://eprints.uwe.ac.uk/18384/1/fox_icra12_submitted.pdf}}</ref> (sensing by local touch only), radar SLAM,<ref>{{cite conference | last1=Marck |first1=J.W. |last2=Mohamoud |first2=A. |last3=v.d. Houwen |first3=E. |last4=van Heijster |first4=R. | title = Indoor radar SLAM A radar application for vision and GPS denied environments. | conference = Radar Conference (EuRAD), 2013 European | year = 2013|url=http://publications.tno.nl/publication/34607287/4nJ48k/marck-2013-indoor.pdf}}</ref> acoustic SLAM,<ref>Evers, Christine, Alastair H. Moore, and Patrick A. Naylor. "[https://spiral.imperial.ac.uk/bitstream/10044/1/38877/2/2016012291332_994036_4133_Final.pdf Acoustic simultaneous localization and mapping (a-SLAM) of a moving microphone array and its surrounding speakers]." 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016.</ref> and wifi-SLAM (sensing by strengths of nearby wifi access points).<ref>Ferris, Brian, Dieter Fox, and Neil D. Lawrence. "[https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-399.pdf Wifi-slam using gaussian process latent variable models]." IJCAI. Vol. 7. No. 1. 2007.</ref> Recent approaches apply quasi-optical [[wireless]] ranging for [[Trilateration|multi-lateration]] ([[Real-time locating system|RTLS]]) or [[Triangulation|multi-angulation]] in conjunction with SLAM as a tribute to erratic wireless measures. A kind of SLAM for human pedestrians uses a shoe mounted [[inertial measurement unit]] as the main sensor and relies on the fact that pedestrians are able to avoid walls to automatically build floor plans of buildings by an [[indoor positioning system]].<ref name=RobertsonEtAl2009>{{cite conference
