Method

Lidar camera fusion network [la] [LidCamNet]


Submitted on 17 Jan. 2018 15:50 by
Luca Caltagirone (Chalmers University of Technology)

Running time:0.15 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Please see paper: https://arxiv.org/abs/1809.07941
Parameters:
Latex Bibtex:
@Article{1809.07941,
Author = {Luca Caltagirone and Mauro Bellone and Lennart Svensson and Mattias Wahde},
Title = {LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks},
journal = {Robotics and Autonomous Systems},
year = 2018,
ee = {https://doi.org/10.101/j.robot.2018.11.002},
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 95.62 % 93.54 % 95.77 % 95.48 % 1.92 % 4.52 %
UMM_ROAD 97.08 % 95.51 % 97.28 % 96.88 % 2.98 % 3.12 %
UU_ROAD 94.54 % 92.74 % 94.64 % 94.45 % 1.74 % 5.55 %
URBAN_ROAD 96.03 % 93.93 % 96.23 % 95.83 % 2.07 % 4.17 %
This table as LaTeX

Behavior Evaluation


This table as LaTeX

Road/Lane Detection

The following plots show precision/recall curves for the bird's eye view evaluation.



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Distance-dependent Behavior Evaluation

The following plots show the F1 score/Precision/Hitrate with respect to the longitudinal distance which has been used for evaluation.


Visualization of Results

The following images illustrate the performance of the method qualitatively on a couple of test images. We first show results in the perspective image, followed by evaluation in bird's eye view. Here, red denotes false negatives, blue areas correspond to false positives and green represents true positives.



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