The KITTI Vision Benchmark Suite

Method

Degeneracy-aware LiDAR-Inertial Odometry with Gaussian Map [DG-LIO]
[Anonymous Submission]

Submitted on 26 Aug. 2024 13:05 by
[Anonymous Submission]

Running time:		0.02 s
Environment:		4 cores @ >3.5 Ghz (C/C++)

Method Description:

A compact and efficient gaussian-based Lidar-
inertial odometry and mapping framework. We
performed a Generalized-ICP(GICP) registration
process, it searches for k-nearest neighbors both
in current and nearby voxels for robust data
association. In map update process, Gaussians from
current scan are either merged to the existed
Gaussians in the map or inserted to the map as new
Gaussians, depending on their probabilistic
distances to the nearest Gaussians in the map.

Parameters:

voxel_size = 0.5m
point_cov = 0.001
max_iteration = 100

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.