Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a ...
Global mobile robot localization is the problem of determining a robot's pose in an environment, using sensor data, when the starting position is unknown. A family of probabi...
A novel particle filter, the Memory-based Particle Filter
(M-PF), is proposed that can visually track moving objects
that have complex dynamics. We aim to realize robustness
aga...
Dan Mikami (NTT), Kazuhiro Otsuka (NTT), Junji YAM...
Abstract— We consider the following problem of decentralized statistical inference: given i.i.d. samples from an unknown distribution, estimate an arbitrary quantile subject to l...
This paper derives a near optimal distributed Kalman filter to estimate a large-scale random field monitored by a network of N sensors. The field is described by a sparsely con...