The KITTI Vision Benchmark Suite

Method

Lidar aided Visual Odometry [la] [LiViOdo]

Submitted on 7 Jun. 2017 21:08 by
Alexander Wilczynski (KIT - MRT)

Running time:		0.5 s
Environment:		1 core @ 2.5 Ghz (C/C++)

Method Description:

Visual Odometry using depth data from LIDAR to
perform combined 3D-2D/2D-2D frame to frame motion
estimation.
No post refinement like BA or Scan Matching.

Parameters:

3D-2D Loss-Value: 0.16

Latex Bibtex:

@ARTICLE{2018arXiv180707524G,
author = {{Graeter}, J. and {Wilczynski}, A. and {Lauer}, M.},
title = "{LIMO: Lidar-Monocular Visual Odometry}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1807.07524},
primaryClass = "cs.RO",
keywords = {Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing},
year = 2018,
month = jul,
adsurl = {https://arxiv.org/pdf/1807.07524.pdf},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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.