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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 ...
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)
The step size is denoted by (sometimes called the learning rate in machine learning) and here ":=" denotes the update of a variable in the algorithm. In many cases, the summand functions have a simple form that enables inexpensive evaluations of the sum-function and the sum gradient.
Machine learning involves the study and construction of algorithms that can learn from and make predictions on data. [3] These algorithms operate by building a model from an example training set of input observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
Machine learningand data mining. A self-organizing map ( SOM) or self-organizing feature map ( SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.
Generative topographic map ( GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking a point in a low ...
Supervised learning. Supervised learning ( SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. [1]
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...