Method

Hyrid stereo SLAM with direct image alignment and features. [HDF-SLAM]


Submitted on 9 Jul. 2017 21:52 by
Radouane Ait Jellal (University of Tübingen)

Running time:0.12 s
Environment:2 cores @ 3.5 Ghz (C/C++)

Method Description:
HDF-SLAM stands for Hybrid with Direct alignment and Features.

We first perform the tracking using ORB-SLAM2 which uses ORB features. Then we refine the final pose using direct image alignment. We use the inverse compositional algorithm for image registration.
We do not use all of the pixels to perform the alignment step (pose refinement), we only use pixels which have locally sufficient textures changes (threshold for the gradient magnitude > 0.1 )
Parameters:
Image alignment:
Maximum number of iterations = 3
Threshold magnitude of gradient = 0.1

Default ORB-SLAM2 parameters:

ORBextractor.nFeatures: 2000
ORBextractor.scaleFactor: 1.2
ORBextractor.nLevels: 8
ORBextractor.iniThFAST: 12
ORBextractor.minThFAST: 7
Latex Bibtex:

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.

Test Set Average


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Sequence 11


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Sequence 12


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Sequence 13


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Sequence 14


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Sequence 15


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