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This is a list of simultaneous localization and mapping (SLAM) methods. The KITTI Vision Benchmark Suite website has a more comprehensive list of Visual SLAM methods. List of methods. EKF SLAM; FastSLAM 1.0; FastSLAM 2.0; L-SLAM (Matlab code) QSLAM; GraphSLAM; Occupancy Grid SLAM; DP-SLAM; Parallel Tracking and Mapping (PTAM)
A map generated by a SLAM Robot. Simultaneous localization and mapping ( SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent 's location within it. While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve ...
Multivariate statistics. Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis ...
Multiple comparisons problem. An example of coincidence produced by data dredging (uncorrected multiple comparisons) showing a correlation between the number of letters in a spelling bee's winning word and the number of people in the United States killed by venomous spiders. Given a large enough pool of variables for the same time period, it is ...
Semantic mapping (statistics) Semantic mapping ( SM) is a statistical method for dimensionality reduction (the transformation of data from a high-dimensional space into a low-dimensional space). SM can be used in a set of multidimensional vectors of features to extract a few new features that preserves the main data characteristics.
The Bernoulli distribution, which takes value 1 with probability p and value 0 with probability q = 1 − p. The Rademacher distribution, which takes value 1 with probability 1/2 and value −1 with probability 1/2. The binomial distribution, which describes the number of successes in a series of independent Yes/No experiments all with the same ...
Motivation. In signal processing, time–frequency analysis [3] is a body of techniques and methods used for characterizing and manipulating signals whose statistics vary in time, such as transient signals. It is a generalization and refinement of Fourier analysis, for the case when the signal frequency characteristics are varying with time.
Simultaneous equations models are a type of statistical model in which the dependent variables are functions of other dependent variables, rather than just independent variables. [1] This means some of the explanatory variables are jointly determined with the dependent variable, which in economics usually is the consequence of some underlying ...