## Method

continuous-time trajectory lidar odometry [la] [Traj-LO]
https://github.com/kevin2431/Traj-LO

Submitted on 27 Apr. 2023 19:20 by
XIN ZHENG (Zhejiang University)

 Running time: 0.1 s Environment: 4 cores @ 3.5 Ghz (C/C++)

 Method Description: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory Parameters: \size_voxel_map=1.0 Latex Bibtex: @ARTICLE{zheng2024traj, author={Zheng, Xin and Zhu, Jianke}, journal={IEEE Robotics and Automation Letters}, title={Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory}, year={2024}, volume={}, number={}, pages={1-8}, doi={10.1109/LRA.2024.3352360}}

## 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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