Drift analysis and sectional post-processing of indoor simultaneous localization and mapping (SLAM)-based laser scanning data

Keitaanniemi, Aino; Rönnholm, Petri; Kukko, Antero; Vaaja, Matti

doi:10.1016/j.autcon.2022.104700

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…Simultaneous Localization And Mapping (based on visual, IMU, or combined) is also being used to eliminate the need for the scanner to be stationary [38][39][40][41][42]. The implementation of such scanners maybe handheld, backpack, car, or drone mounted.…”

Section: Terrestrial Laser Scannersmentioning

confidence: 99%

Geomatic Sensors for Heritage Documentation: A Meta-Analysis of the Scientific Literature

Agapiou,

Skarlatos

2023

Heritage

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This review paper aims to provide a meta-analysis of the scientific literature for heritage documentation and monitoring using geo-information sensors. The study initially introduces the main types of geomatic sensors that are currently widely used for heritage studies. Although the list provided here is indicative rather than exhaustive, it provides a general overview of the variety of sensors used for different observation scales. The study next focuses on the existing literature, based on published documents. Targeted queries were implemented to the Scopus database to extract the relevant information. Filtering was then applied to the results so as to limit the analysis on the specific thematic sub-domains that is applied for heritage documentation and monitoring. These domains include, among other close-range and underwater photogrammetry, Terrestrial Laser Scanner, Unmanned Aerial Vehicles platforms, and satellite observations. In total, more than 12,000 documents were further elaborated. The overall findings are summarized and presented here, providing further insights into the current status of the domain.

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Section: Terrestrial Laser Scannersmentioning

confidence: 99%

Geomatic Sensors for Heritage Documentation: A Meta-Analysis of the Scientific Literature

Agapiou,

Skarlatos

2023

Heritage

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“…Mobil lazer tarama yönteminde arabalar, tekneler, trenler ve el cihazları bulunur. Yersel lazer tarama yönteminde tarayıcı genellikle bir tripodun üzerinde bulunur (Kabadayı & Erdoğan, 2022a;Kabadayı & Erdoğan, 2022b;Keitaanniemi, 2023).…”

Section: Introductionunclassified

El tipi lazer tarayıcı ile küçük objelerin modellenmesi

KAÇARLAR,

ULVİ

2023

LiDAR

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3b haritalama için gerekli veriler ilk zamanlarda total station ve şerit metrelerle toplanıyordu. Uygulama alanı olarak kısıtlıydı. Teknolojik gelişmelerle beraber günümüzde 3b ölçüm teknolojileri, 3b haritalama için gerekli verilerin toplanmasını otomatik hale getirmiştir. Bu tekniklerden son dönemde en çok kullanılanlarından biride yersel lazer tarama tekniğidir. 3b modelleme de lazer tarayıcılar, tarama ortamına bağlı olarak üç kategoriye ayrılabilir. Bunlar havadan lazer tarama, mobil lazer tarama ve yersel lazer taramadır. Kültürel mirasın içerisinde yer alan ve küçük boyuttaki eserlerin modellenmesinde son dönemde kullanılan yöntemlerden biride mobil lazer taramanın içerisinde bulunan el tipi lazer tarama yöntemidir. Bu çalışmada küçük boyuttaki ev biblosu ve vazo objesinin lazer tarama yöntemiyle modellenmesi ve oluşan model üzerinden ölçü alınması anlatılmaktadır. Elde edilen modeller obje yüzeylerindeki renk yoğunlukları, operatörden kaynaklı hatalar ve tarayıcının kısıtlamalarında dolayı nokta bulutu yoğunluğu olarak yeteri yoğunlukta sonuç vermemiştir. Ölçümler elektronik kumpasla ölçülmüş ve kontrol edilmiştir. Üzerinden alınan ölçüler net olarak seçilemediği için ölçüm değerleri hassas çalışmalar için uygun görülmemektedir.

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“…Positioning systems such asVIOQin et al (2018);Forster et al (2014) and SLAM techniquesMur-Artal et al (2015);Zhou et al (2021);Behley and Stachniss (2018);Keitaanniemi et al (2023) usually use camera, Inertial Measurement Unit (IMU)Bai et al (2020) or other sensor configurationRivard et al (2008) to estimate the location. For example, VINS-MonoQin et al (2018) is a real-time optimization-based sliding window SLAM framework that leverages from IMU pre-integration with bias correction and loop detection.…”

mentioning

confidence: 99%

A Probabilistic-based Drift Correction Module for Visual Inertial SLAMs

Navard,

Yilmaz

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Positioning is a prominent field of study, notably focusing on Visual Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM) methods. Despite their advancements, these methods often encounter dead-reckoning errors that leads to considerable drift in estimated platform motion especially during long traverses. In such cases, the drift error is not negligible and should be rectified. Our proposed approach minimizes the drift error by correcting the estimated motion generated by any SLAM method at each epoch. Our methodology treats positioning measurements rendered by the SLAM solution as random variables formulated jointly in a multivariate distribution. In this setting, The correction of the drift becomes equivalent to finding the mode of this multivariate distribution which jointly maximizes the likelihood of a set of relevant geo-spatial priors about the platform motion and environment. Our method is integrable into any SLAM/VIO method as an correction module. Our experimental results shows the effectiveness of our approach in minimizing the drift error by 10× in long treverses.

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Drift analysis and sectional post-processing of indoor simultaneous localization and mapping (SLAM)-based laser scanning data

Cited by 7 publications

References 35 publications

Geomatic Sensors for Heritage Documentation: A Meta-Analysis of the Scientific Literature

Geomatic Sensors for Heritage Documentation: A Meta-Analysis of the Scientific Literature

El tipi lazer tarayıcı ile küçük objelerin modellenmesi

A Probabilistic-based Drift Correction Module for Visual Inertial SLAMs

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