Method

ECTLO: Effective Continuous-time Odometry Using Range Image for LiDAR with Small FoV [la] [ filter-reg]


Submitted on 21 Aug. 2021 04:12 by
Xin Zheng (Zhejiang University)

Running time:0.01 s
Environment:GPU @ 2.6 Ghz (C/C++)

Method Description:
The traditional ICP algorithm is replaced by filter
registration on range image and BEV map. We adjust
our ECTLO method developed for solid-state LiDAR on
the KITTI dataset.
Parameters:
Spherical range image 2048x80.
Bird's-eye-view map 120m x 60m.
Filter window 7x7.
Latex Bibtex:
@inproceedings{zheng2023ectlo,
title={ECTLO: Effective Continuous-Time Odometry
Using Range Image for LiDAR with Small FoV},
author={Zheng, Xin and Zhu, Jianke},
booktitle={IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS)},
year={2023}
}

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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