PDF-Robotic Mapping A Survey Sebastian Thrun February CMUCS School of Computer Science Carnegie

It describes and compares various probabilistic techniques as they are presently being applied to a vast array of mobile robot mapping problems The history of robotic mapping is also described along with an extensive list of open research problems T. cmuedu Abstract In real world planning problems time for deliberation is often limited Anytime planners are well suited for these problems they 64257nd a feasi ble solution quickly and then continually work on improving it until time runs out In this cmuedu ABSTRACT A function on variables is called a junta if it depends on at most of its variables In this article we show that it is possible to test whether a function is a junta or is far from being a junta with k log queries where is the approx cscmuedu thrun In Proceedings of Uncertainty in AI U AI 2002 Abstract In recent years particle 64257lters ha solv ed se eral hard perceptual problems in robotics Early successes of particle 64257lters were limited to lo wdimensional esti mation probl cmuedu nikoscshkuhk haosucsstanfordedu ABSTRACT With the increasing popularity of social networks large volumes of graph data are becoming available Large graphs are also de rived by structure extraction from relational text or scienti64257c data eg cmuedu Adam Wierman Carnegie Mellon University Pittsburgh PA 15213 acwcscmuedu Mor HarcholBalter Carnegie Mellon University Pittsburgh PA 15213 harcholcscmuedu Abstract Workload generators may be classi64257ed as based on a closed system model where scanfd val Carnegie Mellon return y Ax int matvecint A int x int y mallocNsizeofint int i j for i0 i for j0 j yi Aijxj return y brPage 5br Carnegie Mellon int p p mallocNsizeofint for i0 i pi mallocMsizeofint Carnegie Mellon int p p mallocN We present a general methodology for near optimal sensor placement in these and related problems We demonstrate that many realistic outbreak detection objectives eg de tection likelihood population a64256ected exhibit the prop erty of submodularity Floating Point. 15-213: Introduction to Computer Systems – Recitation. January 24, 2011. Today: Floating Point. Data Lab. Floating Point Basics. Representation. Interpreting the bits. Rounding. Floating Point Examples. Preferred Name Guidelines Guiding Principle* Carnegie Mellon University recognizes that students may wish to use a name other than their given names as recorded on offici al university documents. Whe Assembly and Bomb Lab. 15-213: Introduction to Computer Systems . Recitation 4, Sept. 17, 2012. Outline. Assembly. Basics. Operations. Bomblab. Tools. Demo. Carnegie Mellon. Registers. Program counter. Proxylab. and stuff. 15-213: Introduction to Computer Systems. Recitation . 13: November 19, . 2012. Donald Huang (. donaldh. ). Section . M. 2. Carnegie Mellon. Topics. Summary of . malloclab. News. Stacks. 15-213: Introduction to Computer Systems. Recitation 5: September 24, 2012. Joon-Sup Han. Section F. 2. Carnegie Mellon. Today: Stacks. News. Stack discipline review. Quick review of registers and assembly. KeywordsdiametergraphhadoopSymbolDe2nitionGagraphnnumberofnodesinagraphmnumberofedgesinagraphddiameterofagraphBinputbitmasktoHADIRedgerelationoftheinputgraphpairsofnodesuv2GR0re3exiveclosureofR01hnumb Pittsburgh’s leading cash buyer provides much-needed relief for distressed homeowners, helping them sell their properties at attractive prices.