Rapid development of manifold-based graph optimization systems for multi-sensor calibration and SLAM

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Abstract

Non-linear optimization on constraint graphs has recently been applied very successfully in a variety of SLAM backends. We combine this technique with a principled way of handling non-Euclidean spaces, 3D orientations in particular, based on manifolds to build a generic and very flexible framework, the Manifold Toolkit for Matlab (MTKM). We show that MTKM makes it particularly easy to solve non-trivial multi-sensor calibration problems while remaining generic enough to handle a very different class of problems, namely SLAM, as well: After an introductory example on single camera calibration we apply MTKM to calibration of stereo vision and IMU w.r.t. the kinematic chain of a service robot, RGB-D and accelerometer calibration of a Microsoft Kinect, stereo calibration on a Nao soccer robot, and several SLAM benchmark data sets illustrating MTKM's versatility. MTKM and all presented examples are available as open source from http://openslam.org/MTK.html. © 2011 IEEE.

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Wagner, R., Birbach, O., & Frese, U. (2011). Rapid development of manifold-based graph optimization systems for multi-sensor calibration and SLAM. In IEEE International Conference on Intelligent Robots and Systems (pp. 3305–3312). https://doi.org/10.1109/IROS.2011.6048324

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