The KITTI Vision Benchmark Suite

Method

KD-SLAM [KD-SLAM]
https://github.com/rvp-group/kd_slam2

Submitted on 1 Jul. 2026 10:29 by
Giorgio Grisetti (Sapienza univ. of Rome)

Running time:		1e-2 s
Environment:		1 core @ 1.0 Ghz (C/C++)

Method Description:

KD-SLAM a unified lidar pipeline handing 2D/3D SLAM in continuous and discrete time based on PCA KD-Trees as sole primitive

Parameters:

The parameters are in the public repo. Same params for all kitti files.

Latex Bibtex:

@software{grisetti2026kdslam,
author = {Grisetti, Giorgio},
title = {{KD-SLAM: One KD-Tree to deskew them all}},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.},
url = {https://github.com/rvp-group/kd_slam2/blob/main/paper/kd_slam.pdf}
}

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.