actions: []
api_documentation: http://docs.ros.org/groovy/api/ann/html
authors: Sunil Arya and David Mount
brief: ANN
bugtracker: ''
depends: ''
depends_on:
- cognitive_perception
description: "\n\n    ANN is a library written in C++, which supports data structures\
  \ and\n    algorithms for both exact and approximate nearest neighbor searching\
  \ in\n    arbitrarily high dimensions.\n\n    In the nearest neighbor problem a\
  \ set of data points in d-dimensional\n    space is given. These points are preprocessed\
  \ into a data structure, so\n    that given any query point q, the nearest or generally\
  \ k nearest points of\n    P to q can be reported efficiently. The distance between\
  \ two points can be\n    defined in many ways. ANN assumes that distances are measured\
  \ using any\n    class of distance functions called Minkowski metrics. These include\
  \ the\n    well known Euclidean distance, Manhattan distance, and max distance.\n\
  \n    Based on our own experience, ANN performs quite efficiently for point sets\n\
  \    ranging in size from thousands to hundreds of thousands, and in dimensions\n\
  \    as high as 20. (For applications in significantly higher dimensions, the\n\
  \    results are rather spotty, but you might try it anyway.)\n\n    The library\
  \ implements a number of different data structures, based on\n    kd-trees and box-decomposition\
  \ trees, and employs a couple of different\n    search strategies.\n\n    The library\
  \ also comes with test programs for measuring the quality of\n    performance of\
  \ ANN on any particular data sets, as well as programs for\n    visualizing the\
  \ structure of the geometric data structures. \n\n  "
doc_job: doc-groovy-ias_perception
license: LGPL
maintainers: ''
metapackages:
- ias_perception
msgs: []
package_type: package
repo_name: ias_perception
repo_url: ''
srvs: []
timestamp: 1412592402.677664
url: http://www.cs.umd.edu/~mount/ANN/
vcs: git
vcs_uri: https://github.com/code-iai/ias_perception.git
vcs_version: null
