The KITTI Vision Benchmark Suite

Method

Accurate Stereo Visual Odometry Based on Keypoint Selection [st] [KLTVO]

Submitted on 28 May. 2020 14:16 by
Nigel Dias (Federal University of Goiás)

Running time:		0.1 s
Environment:		1 core @ 3.0 Ghz (C/C++)

Method Description:

The method relies on a circular matching for
feature selection using spatial and temporal
information. The process combines the
Illumination Normalized SAD metric for stereo
matching and the KLT algorithm for feature
tracking. In both approaches, we explore the
epipolar geometry constraints to get a fast and
accurate feature correspondence.

Parameters:

KLT win size = 15x15
KLT pyramid max level = 4
NMS win Size = 24x24

Latex Bibtex:

@INPROCEEDINGS{9018624,
author={N. {Dias} and G. {Laureano}},
booktitle={2019 Latin American Robotics Symposium
(LARS), 2019 Brazilian Symposium on Robotics
(SBR) and 2019 Workshop on Robotics in Education
(WRE)},
title={Accurate Stereo Visual Odometry Based on
Keypoint Selection},
year={2019},
volume={},
number={},
pages={74-79},}

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.