Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms.publishedVersio

Landbruksbygninger i Nord-Østerdal, -med fokus på bæresystem

Igjennom tida med bacheloroppgaven har Jostein Utstumo vært innom og dokumentert rundt 70 bygninger av kategorien fjøs m høylåve som er oppført i tidsperioden 1800-1970. Undersøkelsene har foregått i Nord-Østerdalen i Hedmark og forholdt seg til stedene Tynset, Tolga og Os langs glommavassdraget. I Nord-Østerdalen går det et skille mellom østlandske og trønderske tradisjoner i mange kategorier. Bokverket “Beresystem i eldre norske hus” beskriver at det også er et skille i byggetradisjoner og at det er i Tolga. Dokumentasjonsarbeidet i oppgaven viser at det stemmer godt med det påstanden i bokverket. Undersøkelsen viser at det er funnet 8 fotingsrøst i Tynset, 1 fotingsrøst i Tolga og ingen i Os. Når det gjelder sperreverket som er den trønderske tradisjonen er det funnet en rekke bygninger med dette systemet i Tolga og Os, og bare ett i Tynset og dette skiller seg klart ifra de andre funnene av sperreverk i Tolga og Os. Sperreverkfunnet i Tynset ble gjort på Kjæreng Nedre. Denne konstruksjonen er det bygget en kopi av i forbindelse med oppgaven for å redegjør for om gamle bæresystem kan være rasjonelt og fornuftig og benytte til nye bygg

Forholdet mellom straffbar bevisforspillelse og helsepersonells taushetsplikt

Temaet for oppgaven er grensedragningen mellom straffbar bevisforspillelse og helsepersonells taushetsplikt. Ifølge straffeloven § 132 første ledd er det straffbart å motarbeide offentlig undersøkelse eller forfølgning av andres straffbare handlinger ved å sørge for at bevis av betydning for undersøkelsen ”tilintetgjøres, bringes tilside eller forvanskes”. Samtidig skal helsepersonell etter helsepersonelloven § 21 ”hindre at andre får adgang eller kjennskap” til opplysninger om personlige forhold de får vite om i egenskap av å være helsepersonell. Når helsepersonell handler aktivt for å beskytte taushetsbelagte opplysninger, kan det føre til at potensielle bevis i straffesaker blir ødelagt. Dette innebærer at den objektive gjerningsbeskrivelsen i straffeloven § 132 første ledd i utgangspunktet er overtrådt. Det er imidlertid en mulighet for at en slik handling ikke rammes av straffebudet fordi den kan hjemles i taushetsplikten. Hovedproblemstillingene i denne oppgaven er om helsepersonells handlinger som har hjemmel i taushetsplikten kan unnta for straffbar bevisforspillelse, og ved hvilke situasjoner dette i så fall er aktuelt. I redegjørelsen for gjeldende rett vil høyesterettsdommen inntatt i Rt.2013 s.1442 ha betydning. I oppgaven vil det videre problematiseres om gjeldende rett gir en god løsning angående grensedragningen mellom reglene om straffbar bevisforspillelse og helsepersonells taushetsplikt

Implementation and Comparison of Attitude Estimation Methods for Agricultural Robotics

Abstract: The field of precision agriculture increasingly utilize and develop robotics for various applications, many of which are dependent on high accuracy localization and attitude estimation. Special attention has been put towards full attitude estimation by low-cost sensors, in relation to the development of an autonomous field robot. Quaternions have been chosen due to its continuous nature, and with respect to applications in the pipeline with on other platforms. The performance and complexity of two approaches to attitude estimation has been investigated: One Multiplicative Extended Kalman Filter (MEKF) and one non-linear observer. Both were implemented on an ARM Cortex M3 microcontroller with sensors for a Attitude Heading Reference System (AHRS), and benchmarked towards a relative high grade commercial AHRS device. The relative computational burden of the MEKF have been underlined, by execution times more than 10 times those of the non-linear estimator. The implementation complexity is also significantly lower for the non-linear observer, which facilitate test and verification through more transparent software

Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots

Visual Odometry (VO) is increasingly a useful tool for robotic navigation in a variety of applications, including weed removal for agricultural robotics. The methods of evaluating VO are often computationally expensive and can cause the VO measurements to be significantly delayed with respect to a compass, wheel odometry, and GPS measurements. In this paper we present a Bayesian formulation of fusing delayed displacement measurements. We implement solutions to this problem based on the unscented Kalman filter (UKF), leading to what we term an unscented multi-point smoother. The proposed methods are tested in simulations of an agricultural robot. The simulations show improvements in the localization RMS error when including the VO measurements with a variety of latencies

Deep learning based decomposition for visual navigation in industrial platforms

In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms