PROBABILITY LINEAR METHOD POINT CLOUD APPROXIMATION

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F20%3APU138432" target="_blank" >RIV/00216305:26210/20:PU138432 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.engmech.cz/im/im/download/EM2020_proceedings.pdf" target="_blank" >https://www.engmech.cz/im/im/download/EM2020_proceedings.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21495/5896-3-306" target="_blank" >10.21495/5896-3-306</a>

Jazyk výsledku

angličtina

Název v původním jazyce

PROBABILITY LINEAR METHOD POINT CLOUD APPROXIMATION

Popis výsledku v původním jazyce

Fitting curves through point clouds is useful when the further computation is required to be fast or the data set is too large. The most common method to fit a curve into a point cloud is the approximation using the Least squares method (LSM) but it can be used only when the expected data have normal distribution. Data obtained from LIDAR often tend to have an error which can’t be solved by LSM, like data shifted in one angular direction. The main goal of this paper is to propose more efficient method for estimation of obstacle position and orientation. This method uses curve approximation based on probability; this can solve some classic errors that appear when processing data obtained by LIDAR. This method was tested and was found to have a disadvantage: great demand for computing power; its more than ten times slower than classic LSM and in cases with normal distribution gives the same results. It can be used in system where the emphasis is on accuracy or in multiagent solution when working with big data set is not desired.

Název v anglickém jazyce

PROBABILITY LINEAR METHOD POINT CLOUD APPROXIMATION

Popis výsledku anglicky

Fitting curves through point clouds is useful when the further computation is required to be fast or the data set is too large. The most common method to fit a curve into a point cloud is the approximation using the Least squares method (LSM) but it can be used only when the expected data have normal distribution. Data obtained from LIDAR often tend to have an error which can’t be solved by LSM, like data shifted in one angular direction. The main goal of this paper is to propose more efficient method for estimation of obstacle position and orientation. This method uses curve approximation based on probability; this can solve some classic errors that appear when processing data obtained by LIDAR. This method was tested and was found to have a disadvantage: great demand for computing power; its more than ten times slower than classic LSM and in cases with normal distribution gives the same results. It can be used in system where the emphasis is on accuracy or in multiagent solution when working with big data set is not desired.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

ENGINEERING MECHANICS 2020 26th INTERNATIONAL CONFERENCE

ISBN

978-80-214-5896-3

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

306-309

Název nakladatele

Neuveden

Místo vydání

neuveden

Místo konání akce

Online

Datum konání akce

24. 11. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000667956100069