Method

Virtual Vectors for Vision-based SLAM system [la] [lc] [V2-SLAM]
[Anonymous Submission]

Submitted on 29 Apr. 2024 16:58 by
[Anonymous Submission]

Running time:0.07 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We have proposed a SLAM method which aims to
improve the performance in dynamic environment. On
the KITTI dataset, our method uses the monocular
images and point clouds to complete task. The
virtual vector, which is a vector in 3-D space
without a physical entity, is first constructed.
Then, the moving objectives can be identified with
the virtual vectors and be deleted in the whole
SLAM pipeline. After deleting those moving
objectives, our SLAM method can extract feature
points only in the static area and thus achieve a
higher accuracy.
Parameters:
The Mask R-CNN built by PyTorch uses the pretrained
weights.
Latex Bibtex:
No.

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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