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You can also check out ccny_rgbd, which provides tools for fast visual odometry with RGB-D cameras.

You can also check out ccny_rgbd, which provides tools for fast visual odometry with RGB-D cameras.

EDIT as per K_Yousif's question:

I'm not sure if this is also the case with RGBDSLAM, but in our implementation, we've separated the visual odometry from the loop-closing problem. We do provide a mapping interface, which operates on top of the visual odometry and can perform SLAM, but it is not required for the VO.

In terms of the VO, we use a Kalman-Filter based approach which does not require computation of feature descriptors or RANSAC matching. This allows us to use cheap features (such as Lucas-Kanade corners). On an i7 processor, I'm getting a processing time of ~10 to 15ms per frame. Also, our VO has constant time and space requirements (not sure if this is true with RGBDSLAM).

The wiki pages have a bit more information about the pipeline