Method

LiDAR State Estimator Through Sparse Gaussian Process[la] [Traj-LIO]


Submitted on 9 Oct. 2023 06:49 by
XIN ZHENG (Zhejiang University)

Running time:0.1 s
Environment:4 cores @ 2.5 Ghz (C/C++)

Method Description:
State Estimator Through Sparse Gaussian Process
Parameters:
\size_voxel_map=1.0
Latex Bibtex:
@article{zheng2024traj,
title={Traj-LIO: A Resilient Multi-LiDAR Multi-IMU
State Estimator Through Sparse Gaussian Process},
author={Zheng, Xin and Zhu, Jianke},
journal={arXiv preprint arXiv:2402.09189},
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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