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A drawback of the naive implementation of Monte Carlo localization occurs in a scenario where a robot sits at one spot and repeatedly senses the environment without moving. [4] Suppose that the particles all converge towards an erroneous state, or if an occult hand picks up the robot and moves it to a new location after particles have already ...
Monte Carlo simulation: Drawing a large number of pseudo-random uniform variables from the interval [0,1] at one time, or once at many different times, and assigning values less than or equal to 0.50 as heads and greater than 0.50 as tails, is a Monte Carlo simulation of the behavior of repeatedly tossing a coin.
For localization, at least three known reference locations are necessary to localize. Several localization algorithms based on Sequential Monte Carlo (SMC) method have been proposed in literature. [2] [3] Sometimes a node at some places receives only two known locations and hence it becomes impossible to localize. To overcome this problem, dead ...
Monte Carlo methods for particle transport have been driving computational developments since the beginning of modern computers; this continues today. In the 1950s and 1960s, these new methods were organized into a series of special-purpose Monte Carlo codes, including MCS, MCN, MCP, and MCG. These codes were able to transport neutrons and ...
Markov chain Monte Carlo; Marsaglia polar method; Mean-field particle methods; Metropolis light transport; Metropolis-adjusted Langevin algorithm; Metropolis–Hastings algorithm; Monte Carlo integration; Monte Carlo localization; Monte Carlo method for photon transport; Monte Carlo methods for electron transport; Monte Carlo molecular modeling ...
This ray tracing technique uses the Monte Carlo method to accurately model global illumination, simulate different surface characteristics, and capture a wide range of effects observable in a camera system, such as optical properties of lenses (e.g., depth of field and bokeh) or the impact of shutter speed (e.g., motion blur and exposure).
In mathematical statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence [1]), denoted (), is a type of statistical distance: a measure of how much a model probability distribution Q is different from a true probability distribution P.
I will also do a brief literature review and add useful things from various papers. You can view my progress at: User:Dllu/sandbox/Monte Carlo localization (but please do not make changes directly to my sandbox). Depending on how busy I am over the next few days, the rewrite may be completed this weekend, or it may take until the summer.