Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments

Labbé, Mathieu; Michaud, François

doi:10.3389/frobt.2022.801886

Cited by 13 publications

(2 citation statements)

References 49 publications

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“…The research was conducted in the field in July 2022, and SLAM (Simultaneous Localization and Mapping) laser scanning and photographic survey. The SLAM laser scanning was conducted with an ipad Pro, using the inner laser-sensor and the open-source application RTAB-Map (Real-Time Appearance-Based Mapping) developed by a French research team in photogrammetry and robotic for 3D mapping [17,18] and available on github. The photographic survey is composed of 178 photographs taken at the site for SfM-MVS purposes, with a handheld Sony RX100 III Cybershot camera, which is mounted with a Zeiss Vario-Sonnar lense.…”

Section: Methodsmentioning

confidence: 99%

Combining low-cost SLAM and SfM-MVS for Rural and Forest Road-side Slope Erosion Control: a methodology

Gómez¹,

Setiawan²,

Listyaningrum³

et al. 2023

Preprint

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Hillslope and mountain roads are often the source of erosion, which in turn can lead to larger landslides and other types of mass-movements. For this reason, low-cost repeat surveys that can be done by practitioners and not solely scientists and engineers is essential. To solve this issue the present contribution shows the testing and applicability of a mix of SfM-MVS and low-cost SLAM technology to provide erosion information. The low-cost SLAM generated between 300 ~ 900 points per square meter, while SfM-MVS created between ~11,000 to 900,000 points. The density is however a trade-off against a spatially varying error, although the variability at the road-cut scale only ranges from 2 cm vertically to 2.5 cm in x,y,z based on the C2C algorithm. Furthermore, this error tends to be more important in the higher section of the cutout (further away from the sensor and at a flatter angle). It is thus possible to measure small-scale roadside change, providing that the change is in the range of >5 cm (adding the maximum potential error twice) and providing that the road-cut is such that the sensors can be brought close to the surface (camera or sensor on a pole). The authors suggest that it can be a solution for state and agencies with limited funding and that cannot afford regular laser or for roadside that are difficult to access.

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Section: Methodsmentioning

confidence: 99%

Combining low-cost SLAM and SfM-MVS for Rural and Forest Road-side Slope Erosion Control: a methodology

Gómez¹,

Setiawan²,

Listyaningrum³

et al. 2023

Preprint

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“…Finally in our survey, we shall consider a multi-faceted field of computer vision called illumination invariance. The research of this topic covers diverse tasks, such as object detection [65], SLAM [36], and face recognition [86], but the essential question remains roughly the same across them: how can we extract the same information under illumination changes. While the question may very well seem to cover the specularities, the field is more focused on seasonal, daily or situational human-made changes in illumination rather than specularities, which are a direct effect of the prevailing illumination while not being a source of illumination or a change in the sources per se.…”

Section: Other Fieldsmentioning

confidence: 99%

Non-Lambertian surfaces and their challenges for visual SLAM

Pyykölä,

Joswig,

Ruotsalainen

2023

Preprint

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Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities. These specularities pose a potential issue in industrial SLAM, where non-Lambertian surfaces are aplenty naturally, as specularities can violate two common and basic assumptions of computer vision, namely the brightness and colour constancies, which assume that the brightness and colour of same real-world points stays constant across images. The performance of industrial navigation systems can as such be hindered by specularities. This paper reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ORB SLAM 3 is performed on the data. The paper concludes with discussion about the results of empirical evaluation and the findings of the survey.

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