Method

DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration [la] [DeepCLR]


Submitted on 7 Mar. 2020 13:22 by
Markus Horn (Ulm University, Institute of Measurement, Control and Microtechnology)

Running time:0.05 s
Environment:GPU @ 1.0 Ghz (Python + C/C++)

Method Description:
Deep point cloud registration for LiDAR data based on set abstraction for input processing and flow embedding for registration estimation.
Parameters:
n_fps = 1024
SA radii = 0.5, 1.0
n_s,sa = 512, 1024
FE radius = 10.0
n_s,fe = 15
Loss beta = 200
MLP_sa = [16, 16, 32]
MLP_fe = [128, 128, 256]
MLP_pn = [256, 512, 512, 1024]
MLP_fc = [512, 256, 8]
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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