Properties of Pressure Vertical to the surface and point into it. At any point, pressure is independent of orientation

Outline Lightning Summary Black Box Model of SIFT SLAM Vision System Challenges in Computer Vision What these challenges mean for visual SLAM How SIFT extracts candidate landmarks How landmarks are tracked in SIFT SLAM Alternative vision-based SLAM systems Open questions

Leslie Pack Kaelbling, Michael. Littman and Anthony R. Cassandra, \Planning and Acting in Partially Observable Stochastic Domains,\ Artificial Intelligence, Vol. 101, 1998. j. Pineau,g. Gordon&s. Thrun\Point-based- value iteration: An anytime algorithm for POMDPs\. International Joint Conference on Artificial Intelligence(JCAI) Acapulco, Mexico. Aug

Fast Solutions to CSp's Based on PROSSER, P. Hybrid algorithms for the constraint satisfaction problem. Computational Intelligence

Fast Solutions to CSPs Presented by: Robert Effinger Dan Lovell Presented To: 16.412J Cognitive Robotics MIT References: \Dynamic Backtracking\ Matthew L. Ginsberg, CIRL, University of Oregon Journal of Artificial Intelligence Research 1(1993)

Mapping Topics: Topological Maps SLAM HMMs Revisited Additional reading: B. J. Kuipers Y.-T. Byun. 1991. \A robot exploration and mapping strategy based on a semantic hierarchy of spatial rep rstations. jour. s and

Fault Aware Systems: Model-based Programming and Diagnosis Brian C. Williams 16.412J/6.834J March 8th, 2004 courtesy of JPL Four launches in 7 months MERS CSAIL

Temporal Planning in Space Brian C. Williams 16.412J/6.834J based on \Handling Time: February 23rd, 2004 Constraint-based Interval Planning,\ by David E. Smith Outline Operational Planning for the Mars Exploration Rovers

Foundations of State Estimation PartⅡ Topics: Hidden Markov Models Particle Filters Additional reading: L. R. Rabiner, \ tutorial on hidden Markoy models,\ Proceedings of the IEEE, vol. 77,. 257-286, 1989 Radford M. Neal, 1993. Probabilistic Inference Using Markov Chain Monte

Probabilistic roadmaps Planning in the real world Planning amidst moving obstacles RRT-based planners Conclusions Applicability of Lazy PRM to Spheres