Method

LiDAR SLAM Using a Point-Based Implicit Neural Representation [la] [lc] [PIN-SLAM]
https://github.com/PRBonn/PIN_SLAM

Submitted on 9 Nov. 2023 11:20 by
Yue Pan (University of Bonn)

Running time:0.1 s
Environment:GPU @ >3.5 Ghz (Python)

Method Description:
PIN-SLAM is a full-fledged implicit neural LiDAR
SLAM system including odometry, loop closure
detection, and globally consistent mapping
Parameters:
TBD
Latex Bibtex:
@article{pan2024tro,
author = {Y. Pan and X. Zhong and L. Wiesmann and
T. Posewsky and J. Behley and C. Stachniss},
title = {{PIN-SLAM: LiDAR SLAM Using a Point-Based
Implicit Neural Representation for Achieving Global
Map Consistency}},
journal = {IEEE Transactions on Robotics (TRO)},
year = {2024},
}

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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