The KITTI Vision Benchmark Suite

Method

Exactly Sparse Delayed State Filter on Lie groups for Long-term Pose Graph SLAM [st] [lc] [LG-SLAM]

Submitted on 28 Sep. 2018 18:27 by
Kruno Lenac (FER)

Running time:		0.2 s
Environment:		4 cores @ 2.5 Ghz (C/C++)

Method Description:

In this paper we propose a SLAM back-end
solution called the exactly sparse delayed state
filter on Lie groups
(LG-ESDSF). We derive LG-ESDSF and demonstrate
that it retains all the good characteristics of
the classic
Euclidean ESDSF—main being the exact sparsity of
the information matrix. The key advantage of LG-
ESDSF in
comparison to the classic ESDSF lies in the
ability to respect the state space geometry by
negotiating uncertainties
and employing filtering equations directly on
Lie groups.

Parameters:

dmin = 50m or 80m

Latex Bibtex:

@article{doi:10.1177/0278364918767756,
author = {Kruno Lenac and Josip Ćesić and Ivan
Marković and Ivan Petrović},
title ={Exactly sparse delayed state filter on
Lie groups for long-term pose graph SLAM},
journal = {The International Journal of Robotics
Research},
volume = {37},
number = {6},
pages = {585-610},
year = {2018},
doi = {10.1177/0278364918767756},

URL = {
https://doi.org/10.1177/0278364918767756

},
eprint = {
https://doi.org/10.1177/0278364918767756

}

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.