Improving the measurement strategies and post-processing of Simultaneous Localization and Mapping based handheld laser scanning

Aino Keitaanniemi

Improving the measurement strategies and post-processing of Simultaneous Localization and Mapping based handheld laser scanning

Julkaisun otsikon käännös: Samanaikaisesti paikantavien ja kartoittavien käsilaserkeilainten mittausstrategian ja jälkiprosessoinnin parantaminen

Aino Keitaanniemi

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Kolmiulotteiset mittausteknologiat kuten laserkeilaus ovat automatisoineet 3D-kartoituksen. Nykyisillä menetelmillä on kuitenkin ongelmia ympäristöissä, joihin satelliittipaikannusjärjestelmä ei ylety. Maalaserkeilaus (TLS) näissä ympäristöissä on työlästä, aikaa vievää ja aineistoon jää helposti aukkoja. Tällöin voidaan hyödyntää samanaikaisesti paikantavia ja kartoittavia (SLAM) laser-keilaimia. Valitettavasti, SLAM-laserkeilaimien mittausmenetelmien ja jälkiprosessoinnin vaikutuksia ei ole juurikaan arvioitu. Väitöskirjassa tutkittiin ja kehitettiin SLAM-laserkeilaimien soveltuvuutta rakennetun ympäristön 3D-kartoittamiseen. Tämä toteutettiin hypoteesin avulla, jonka mukaan SLAM-laserkeilaimien mittausreittien ja toimintatapojen kuten aineistojen yhdistämisen ja jälkiprosessoinnin kehittämisellä ne soveltuvat paremmin 3D-kartoitukseen. Mittausreittejä ja toimintatapoja tutkittiin kahdessa käyttötapauksessa hyödyntäen kahta kaupallista SLAM-laserkeilainta. Kokeiluissa tutkittiin mittausreitin ja ympäristön piirteiden vaikutusta SLAM-laserkeilaukseen sekä integraatiomenetelmää, jossa SLAM-laserkeilauksen pistepilveä hyödynnettiin tukiaineistona TLS-pistepilvien rekisteröinnissä. Lisäksi tutkittiin paikannuksen drift virheen pienentämistä jälkiprosessoinnissa. Työn tulokset osoittivat useita SLAM-laserkeilausmittausten suorituskyvyn kehitystapoja. Mittausreitin tulisi sisältää ympäristöstä riippuen sisäisiä kierroksia. Vakaissa ympäristöissä saadaan paras pistepilven tarkkuus tekemällä mahdollisimman vähän sisäisiä kierroksia, kun taas epävaikaissa ympäristöissä niitä tarvitaan enemmän. Monissa rakennetun ympäristön kohteissa mittausreitti koostuu näiden yhdistelmästä. Lisäksi mittausreitin sisäisten kierroksien tulisi kiertää ympäristön kohteita ja halkaisijan koko tulisi valita ympäristön mukaan. Näiden lisäksi SLAM-laserkeilaimen näkymäalue tulisi suunnata sisäisen kierroksen keskelle. Ympäristöjä, jotka sisältävät liikettä, epätasaisesti jakautuneita maamerkkejä, joita SLAM-algoritmi hyödyntää kartoituksessa tai avoimia alueita, tulisi välttää SLAM-laserkeilaimilla. Mikäli välttäminen ei ole mahdollista, SLAM-laser- keilaimen maksimimittausetäisyys tulisi valita tarpeen mukaan tai ympäristöön tulisi lisätä maamerkkejä. Näiden avulla voidaan välttää vääristymiä. Kuitenkin vääristymät olivat yksisuuntaisia ja niitä voidaan vähentää SLAM-laserkeilaimien pistepilvistä jälkiprosessointimenetelmillä. SLAM-laserkeilaimen ja TLS yhdistämisen tuloksien pohjalta 3D-rekonstruktioita voidaan tuottaa tarpeen mukaan optimoiduilla tarkkuuksilla ja työssä tehdyssä koetyössä mittaamiseen kului 72% vähemmän aikaa. Työn tulosten mukaan mittausreittejä ja toimintatapoja kehittämällä SLAM-laserkeilaimia voidaan hyödyntää rakennetun ympäristön 3D-kartoitukseen entistä tehokkaammin, jolloin erilaisten 3D-mittaussovellusten vaatimukset voidaan saavuttaa paremmin.

Julkaisun otsikon käännös	Samanaikaisesti paikantavien ja kartoittavien käsilaserkeilainten mittausstrategian ja jälkiprosessoinnin parantaminen
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Vaaja, Matti Tapio, Vastuuprofessori Hyyppä, Hannu, Ohjaaja Rönnholm, Petri, Ohjaaja Kukko, Antero, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-64-1270-2
Sähköinen ISBN	978-952-64-1271-9
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

samanaikaisesti paikantava ja kartoittava
laserkeilaus
3D-rekonstruktio
rakennettu ympäristö
pistepilvi
mittausreitti
3D-kartoitus

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-64-1271-9

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Koko teksti

3D-studio (i3)
Matti Kurkela (Manager)
Rakennetun ympäristön laitos
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Insinööritieteiden korkeakoulu
Laitteistot/tilat: Facility

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title = "Improving the measurement strategies and post-processing of Simultaneous Localization and Mapping based handheld laser scanning",

abstract = "Three-dimensional (3D) data technologies such as laser scanning have automated the data collection of 3D mapping. However, built environments include ones without global navigation satellite system access, and where terrestrial laser scanning of the environment would be laborious, time-consuming, and prone to occlusions. In these environments 3D mapping could instead be executed with simultaneous localization and mapping (SLAM) laser scanners (LSs). Regrettably, the effects of the these measurement strategies and post-processing of SLAM LS systems have been rarely evaluated. This dissertation investigated and developed the workflow of SLAM LS systems for 3D mapping in a built environment, with the hypothesis that by evolving measurement paths and practices as data integration and post-processing methods, SLAM LS systems are more suitable to be applied for 3D mapping. The measurement paths and practices were studied in use cases with two commercial SLAM LS systems. The experiments investigated the effect of the measurement paths and existing features in the environment. The integration method utilized a SLAM LS point cloud as supporting data for terrestrial laser scanner (TLS) point cloud registration. In addition, the post-processing was investigated by demonstrating the drift error reduction. Based on the results, the measurement paths and practices can be evolved with the utilization of a well-planned measurement path. The measurement path should include internal loops based on the environment that it is intended to measure. Stable environments have better point cloud ac- curacy with a minimal number of internal loops. In contrast, unstable environments require more internal loops. The overall path in most built environments should combine these practices. In addition, the internal loops should be located around the landmarks, the diameter of the internal loop should be scaled beside the space, and SLAM LS should be facing the middle of the environment during the internal loop. The environments include dynamic objects, the uneven distribution of the landmarks for the SLAM algorithm or unembellished should be avoided. If avoiding such environment is impossible, the measurement range of SLAM LS should be chosen accordingly, or additional landmarks should be added. These can prevent the drift error from occurring. However, the drift error appeared to be onedimensional and can be reduced with commercial software from the SLAM LS point cloud. The results indicate that the integration of SLAM LS and TLS pro- moted the 3D reconstruction with accuracy optimization in required locations and a reduction of measurement time by 72%. With the evolved measurement paths and practices, the SLAM LS systems could be utilized for 3D mapping in built environments. The requirements of the 3D reconstructions can be reached by understanding the measurement methods of the SLAM LS.",

keywords = "simultaneous localization and mapping, laser scanning, 3D reconstruction, built environment, point cloud, measurement path, 3D, 3D mapping, samanaikaisesti paikantava ja kartoittava, laserkeilaus, 3D-rekonstruktio, rakennettu ymp{\"a}rist{\"o}, pistepilvi, mittausreitti, 3D-kartoitus, simultaneous localization and mapping, laser scanning, 3D reconstruction, built environment, point cloud, measurement path, 3D, 3D mapping",

author = "Aino Keitaanniemi",

year = "2023",

language = "English",

isbn = "978-952-64-1270-2",

series = "Aalto University publication series DOCTORAL THESES",

publisher = "Aalto University",

number = "72",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Improving the measurement strategies and post-processing of Simultaneous Localization and Mapping based handheld laser scanning

AU - Keitaanniemi, Aino

PY - 2023

Y1 - 2023

N2 - Three-dimensional (3D) data technologies such as laser scanning have automated the data collection of 3D mapping. However, built environments include ones without global navigation satellite system access, and where terrestrial laser scanning of the environment would be laborious, time-consuming, and prone to occlusions. In these environments 3D mapping could instead be executed with simultaneous localization and mapping (SLAM) laser scanners (LSs). Regrettably, the effects of the these measurement strategies and post-processing of SLAM LS systems have been rarely evaluated. This dissertation investigated and developed the workflow of SLAM LS systems for 3D mapping in a built environment, with the hypothesis that by evolving measurement paths and practices as data integration and post-processing methods, SLAM LS systems are more suitable to be applied for 3D mapping. The measurement paths and practices were studied in use cases with two commercial SLAM LS systems. The experiments investigated the effect of the measurement paths and existing features in the environment. The integration method utilized a SLAM LS point cloud as supporting data for terrestrial laser scanner (TLS) point cloud registration. In addition, the post-processing was investigated by demonstrating the drift error reduction. Based on the results, the measurement paths and practices can be evolved with the utilization of a well-planned measurement path. The measurement path should include internal loops based on the environment that it is intended to measure. Stable environments have better point cloud ac- curacy with a minimal number of internal loops. In contrast, unstable environments require more internal loops. The overall path in most built environments should combine these practices. In addition, the internal loops should be located around the landmarks, the diameter of the internal loop should be scaled beside the space, and SLAM LS should be facing the middle of the environment during the internal loop. The environments include dynamic objects, the uneven distribution of the landmarks for the SLAM algorithm or unembellished should be avoided. If avoiding such environment is impossible, the measurement range of SLAM LS should be chosen accordingly, or additional landmarks should be added. These can prevent the drift error from occurring. However, the drift error appeared to be onedimensional and can be reduced with commercial software from the SLAM LS point cloud. The results indicate that the integration of SLAM LS and TLS pro- moted the 3D reconstruction with accuracy optimization in required locations and a reduction of measurement time by 72%. With the evolved measurement paths and practices, the SLAM LS systems could be utilized for 3D mapping in built environments. The requirements of the 3D reconstructions can be reached by understanding the measurement methods of the SLAM LS.

AB - Three-dimensional (3D) data technologies such as laser scanning have automated the data collection of 3D mapping. However, built environments include ones without global navigation satellite system access, and where terrestrial laser scanning of the environment would be laborious, time-consuming, and prone to occlusions. In these environments 3D mapping could instead be executed with simultaneous localization and mapping (SLAM) laser scanners (LSs). Regrettably, the effects of the these measurement strategies and post-processing of SLAM LS systems have been rarely evaluated. This dissertation investigated and developed the workflow of SLAM LS systems for 3D mapping in a built environment, with the hypothesis that by evolving measurement paths and practices as data integration and post-processing methods, SLAM LS systems are more suitable to be applied for 3D mapping. The measurement paths and practices were studied in use cases with two commercial SLAM LS systems. The experiments investigated the effect of the measurement paths and existing features in the environment. The integration method utilized a SLAM LS point cloud as supporting data for terrestrial laser scanner (TLS) point cloud registration. In addition, the post-processing was investigated by demonstrating the drift error reduction. Based on the results, the measurement paths and practices can be evolved with the utilization of a well-planned measurement path. The measurement path should include internal loops based on the environment that it is intended to measure. Stable environments have better point cloud ac- curacy with a minimal number of internal loops. In contrast, unstable environments require more internal loops. The overall path in most built environments should combine these practices. In addition, the internal loops should be located around the landmarks, the diameter of the internal loop should be scaled beside the space, and SLAM LS should be facing the middle of the environment during the internal loop. The environments include dynamic objects, the uneven distribution of the landmarks for the SLAM algorithm or unembellished should be avoided. If avoiding such environment is impossible, the measurement range of SLAM LS should be chosen accordingly, or additional landmarks should be added. These can prevent the drift error from occurring. However, the drift error appeared to be onedimensional and can be reduced with commercial software from the SLAM LS point cloud. The results indicate that the integration of SLAM LS and TLS pro- moted the 3D reconstruction with accuracy optimization in required locations and a reduction of measurement time by 72%. With the evolved measurement paths and practices, the SLAM LS systems could be utilized for 3D mapping in built environments. The requirements of the 3D reconstructions can be reached by understanding the measurement methods of the SLAM LS.

KW - simultaneous localization and mapping

KW - laser scanning

KW - 3D reconstruction

KW - built environment

KW - point cloud

KW - measurement path

KW - 3D

KW - 3D mapping

KW - samanaikaisesti paikantava ja kartoittava

KW - laserkeilaus

KW - 3D-rekonstruktio

KW - rakennettu ympäristö

KW - pistepilvi

KW - mittausreitti

KW - 3D-kartoitus

KW - simultaneous localization and mapping

KW - laser scanning

KW - 3D reconstruction

KW - built environment

KW - point cloud

KW - measurement path

KW - 3D

KW - 3D mapping

M3 - Doctoral Thesis

SN - 978-952-64-1270-2

T3 - Aalto University publication series DOCTORAL THESES

PB - Aalto University

ER -

Improving the measurement strategies and post-processing of Simultaneous Localization and Mapping based handheld laser scanning

Abstrakti

Tutkimusalat

Pääsy asiakirjaan

OpenUrl-saatavuus

Sormenjälki

Laitteet

3D-studio (i3)

i3 – Industry Innovation Infrastructure

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