Method

Deep LiDAR Odometry and Mapping [la] [DLOAM]


Submitted on 11 Jul. 2019 09:14 by
Deyu Yin (Wuhan University)

Running time:2 s
Environment:8 cores @ 3.5 Ghz (Python)

Method Description:
Using deep-learning to extract keypoints and
features based on spherical-ring point cloud and
voxel model.
A refine ICP-matching is applied for keyframes.
Parameters:
VoxelSize = 0.02m
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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