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Alejo Concha Belenguer
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+Javier Civera and me are pleased to release RGBDTAM, a direct RGB-D SLAM that runs in real-time and on a CPU.

The key ingredients of our approach are mainly two. Firstly, we evaluate different configurations for the error minimization in the 6DoF pose estimation. Specifically, these different configurations are related to dense vs semi-dense, photometric error vs geometric error and depth vs inverse depth error (in the geometric term). We show that the fusion of a semi-dense photometric and dense geometric (inverse depth) error for the pose optimization (Figure 1 in the paper) is the most accurate option. And secondly, we model the multi-view constraints and their errors in the mapping and pose tracking threads, which adds extra information over other approaches.

We compare our system against ElasticFusion in the TUM dataset.

Paper: https://arxiv.org/pdf/1703.00754.pdf
Source code: https://github.com/alejocb/rgbdtam
Video: https://www.youtube.com/watch?v=sc-hqtJtHD4

We hope this software is useful.
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+Javier Civera  and me are pleased to release the source code for our dense monocular odometry algorithm, called DPPTAM.
It is described in our IROS 2015 paper:

[1] Alejo Concha, Javier Civera. DPPTAM: Dense Piecewise Planar Tracking and Mapping from a Monocular Sequence. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS15), Hamburg, Germany, 2015


DPPTAM is a direct monocular odometry algorithm that estimates a dense reconstruction of a scene in real-time on a CPU. Highly textured image areas are mapped using standard direct mapping techniques, that minimize the photometric error across different views. We make the assumption that homogeneous-color regions belong to approximately planar areas. 


The source code is released under GPLv3 license. For details please view the licence file in the source code.

Source code:  https://github.com/alejocb/dpptam 
Video:  https://www.youtube.com/watch?v=1R3DkwKrWiI

We hope that people find it useful.
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