https://github.com/ori-drs/isam

iSAM is an optimization library for sparse nonlinear problems as encountered in simultaneous localization and mapping (SLAM).
https://github.com/ori-drs/isam

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iSAM is an optimization library for sparse nonlinear problems as encountered in simultaneous localization and mapping (SLAM).

• Host: GitHub
• URL: https://github.com/ori-drs/isam
• Owner: ori-drs
• License: lgpl-2.1
• Archived: true
• Created: 2016-02-23T15:27:26.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2017-03-01T17:06:02.000Z (almost 8 years ago)
• Last Synced: 2024-08-01T21:42:15.944Z (6 months ago)
• Language: C++
• Size: 2.22 MB
• Stars: 100
• Watchers: 10
• Forks: 39
• Open Issues: 1
• Metadata Files:
  • Readme: README
  • Changelog: ChangeLog
  • License: LICENSE

Awesome Lists containing this project

• awesome-and-novel-works-in-slam - [Link

README

        

Incremental Smoothing and Mapping (iSAM) library

Michael Kaess, Hordur Johannsson, David Rosen, John Leonard, 2012

isamlib/  - Source code for the iSAM library

include/  - Header files for the iSAM library

isam/     - Source code for main iSAM executable

examples/ - Example code for iSAM

doc/      - Documentation (after calling "make doc")

misc/     - Code referenced from publications

data/     - Example data files for 2D and 3D

lib/      - iSAM library (after calling "make")

bin/      - Executables (after calling "make")

Type "make doc" to generate the full source documentation including

installation instructions and examples. Then point your browser to:

doc/html/index.html

The latest documentation is also available online at:

http://people.csail.mit.edu/kaess/isam/

--------------

Overview:

The iSAM library provides a range of existing functionality for

least-squares optimization, focused on the SLAM problem (e.g. 2D/3D

pose graph, landmarks, visual SLAM). In addition to standard batch

optimization, efficient incremental optimization is provided for sets

of variables (nodes) and constraints (factors) that grow over

time. The library performs (incremental) QR matrix factorization to

solve the normal equations. Nonlinear constraints are dealt with using

Gauss-Newton, Powell's Dog leg or, for batch only, the

Levenberg-Marquardt algorithm. The default quadratic cost function can

be replaced by a general cost function, allowing the use of robust

estimators.

Beyond the provided functionality, the library can easily be extended

by the user to other sparse least-squares problems. In particular,

the user can define new nodes, containing variables to be estimated as

well as an exponential map for dealing with over-parameterized

representations. Similarly, the user can define new factors,

containing the error function for a given constraint and optionally a

symbolic Jacobian for cases in which the provided numerical

differentiation is not sufficiently fast.