SLoM - Sparse Least Squares on Manifolds

The SLoM-Framework provides a framework to optimize arbitrary Least-Square Problems on Manifolds. This version of SLoM is out-dated and merely provided for historic reasons. SLoM is now part of the Manifold ToolKit (MTK) which is also available on OpenSLAM.
Further information

Christoph Hertzberg; Udo Frese;

Get the Source Code via SVN
svn co

Long Description
The SLoM framework originated from a diploma thesis (see papers below). It provides a framework to optimize arbitrary least-squares problems on manifolds, which includes most feature based SLAM-Problems. So far the framework only provides the actual optimization part, i.e. for sensor models etc have to be modeled and implemented for each particular problem. It features some examples for common problems like pure pose-relation or odometry and landmarks. One of the advantages of SLoM is that it can solve not only SLAM problems, but also other least-square problems like calibration problems.

Input Data
SLoM only provides a framework, so many kinds of input data can be used.

Logfile Format
SLoM only provides a framework, so any logformat for which a reader exists can be used.

Type of Map
SLoM can be used to generate any kind of feature based maps.

Hardware/Software Requirements
SLoM was developed and tested under gcc 3.4.4 and 4.3.2.
SLoM requires the CSparse package available at

Papers Describing the Approach
C. Hertzberg: A Framework for Sparse, Non-Linear Least Squares Problems on Manifolds, Diploma Thesis, University of Bremen, 2008 (link)

License Information
This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
The authors allow the users of to use and modify the source code for their own research. Any commercial application, redistribution, etc has to be arranged between users and authors individually and is not covered by

For further license information, please contact the authors.

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