The KITTI Vision Benchmark Suite

Method

Voxelized Gaussian Mixture Model LiDAR Odometry [la] [VGMM-LO]
[Anonymous Submission]

Submitted on 15 May. 2026 18:54 by
[Anonymous Submission]

Running time:		0.05 s
Environment:		GPU @ 1.0 Ghz (Python)

Method Description:

For this method, a representation similar to 3D-
NDT is used, but instead of a single Gaussian per
voxel, a GMM with multiple Gaussians is
constructed in every voxel. The registration of
the points is performed with the Expectation-
Maximization algorithm. With this, each point is
assigned a probability of belonging to neighboring
Gaussians, instead of a hard assignment.

Parameters:

GMM Map: "cell_size": 1.0, "grid_dim_x": 100,
"grid_dim_y": 100, "grid_dim_z": 12
Registration: kernel_param: 0.3, subsampling_voxel:
0.2

Latex Bibtex:

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.