Visual Odometry for Vehicles’ Undercarriage 3D Modelling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332334" target="_blank" >RIV/68407700:21230/19:00332334 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/19:00332334

Result on the web

<a href="https://doi.org/10.1007/978-3-030-14984-0_9" target="_blank" >https://doi.org/10.1007/978-3-030-14984-0_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-14984-0_9" target="_blank" >10.1007/978-3-030-14984-0_9</a>

Result language

angličtina

Original language name

Visual Odometry for Vehicles’ Undercarriage 3D Modelling

Original language description

This work describes a part of a project developing a vehicles’ undercarriage security scanner based only on cameras. The scanner is used to a security check of a vehicle’s undercarriage and is typically installed at an entrance to a strategic compund. The security scanner reconstruct a 3D model of a vehicle’s undercarriage from a sequence of a multi-camera stereo images. To get a complete model we need to stitch particular parts of the 3D model via transformations between particular vehicle positions in which images are captured. The method for getting these transformations is presented in this paper. The task of computing trajectory from a sequence of camera images is called visual odometry (VO). Usually, the camera is placed on a moving object and tracks its position. In our case, the camera is fixed and viewing a moving vehicle, but the task is the same. In the first part, there is a comparison of feature detectors and their parameters based on experimental data because the images properties of undercarriage are different from ordinary surroundings used by the most of VO methods. Undercarriages do not contain a lot of features, and there are many low-textured surfaces. In the second part, the proposed VO method is described. It is based on the best feature from the first part, which serves to search corresponding points between images. It uses 3D to 2D VO method to compute the transformation between consecutive frames. In this method, 3D points are triangulated from the previous pair of stereo camera images, and they are reprojected to one of the actual images. The method finds the transformation of 3D positions which minimizes reprojection error. The method was developed with respect to requirement of almost real-time computing time and low-texture environment. Finally, this method was evaluated on realistic data acquired with ground truth position.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/VI20172020080" target="_blank" >VI20172020080: Kassandra - multi-camera vehicles´ undercarriage security scanner</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Modelling and Simulation for Autonomous Systems (MESAS 2018)

ISBN

978-3-030-14983-3

ISSN

0302-9743

e-ISSN

—

Number of pages

10

Pages from-to

111-120

Publisher name

Springer International Publishing AG

Place of publication

Cham

Event location

Praha

Event date

Oct 17, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000554861000009