K. Murphy. Bayesian map learning in dynamic environments. In Neural Info. Proc. Systems (NIPS), 1999. Home/Search Document Details and Download
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....not actually hold: for example, it would be violated if we knew that opponents tended to move in groups. In practice, though, assuming independence in opponent motion is safe in that it establishes a worst case motion model. Independence also holds for measurements, as discussed at length in [19]. In particular, there are two types of information we can get from our sensors, positive and negative. Positive information tells us where an opponent actor is; we receive positive information by associating a sensor reading i to a role j. Negative information tells us where an opponent is not; ....
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....as an efficient approach to SLAM based on particle filtering [6] does not fall into either of the categories above. FastSLAM takes advantage of an important characteristic of the SLAM problem (with known data association) landmark estimates are conditionally independent given the robot s path [17] . FastSLAM uses a particle filter to sample over robot paths. Each particle possesses N low dimensional EKFs, one for each of the N landmarks. This representation requires O(NM) memory, where M is the number of particles in the particle filter. Updating this filter requires O(M log N) time, with ....
....noise: p(z t j s t ; n t ) g(s t ; n t ) t (2) p(s t j u t ; s t 1) h(u t ; s t 1) t (3) Here g and h are nonlinear functions, and t and t are Gaussian noise variables with covariance R t and P t , respectively. 3 FastSLAM FastSLAM [15] is based on the important observation [17] that the posterior can be factored ) p(s Y n p( n j s ) 4) This factorization is exact and universal in SLAM problems. It states that if one (hypothetically) knew the path of the vehicle, the landmark positions could be estimated independently of each other (hence ....
K. Murphy. Bayesian map learning in dynamic environments. Proc. NIPS, 1999.
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K. Murphy. Bayesian map learning in dynamic environments. In Neural Info. Proc. Systems (NIPS), 1999.
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