Construction Inspection through Spatial Database

Hasan, A.; Qadir, Ashraf; Nordeng, Ian; Neubert, Jeremiah

doi:10.48550/arxiv.1611.03566

Cited by 1 publication

(2 citation statements)

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“…With the development of more advanced 3D sensing technologies, navigation methods based on 3D sensors, such as stereo-vision [16], LiDAR [17], and RGB-D camera [18], can directly utilize 3D points with metric information in the estimation process, therefore can provide improved localization and reconstruction results. In addition to the most commonly used point features, line and plane features are also adopted as additional features in some particular mission scenarios, such as indoor servicing [19], [20], structure inspection [21], [22], and autonomous landing [23], in case that point features are not sufficient.…”

Section: Related Workmentioning

confidence: 99%

“…It is practical wisdom to implement more general structure prior information of the environment to improve the localization and mapping quality rather than to consider the environment as entirely unknown. For instance, in a building inspection environment, the structure information, as elaborate as the CAD modes, or as coarse as some common sense such as flat planes, parallel lines, with proper parameterization, can be implemented to aid the SLAM process [21], [39].…”

Section: Related Workmentioning

confidence: 99%

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SPINS: Structure Priors aided Inertial Navigation System

Lyu¹,

Liu

Cao³

et al. 2020

Preprint

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Although Simultaneous Localization and Mapping (SLAM) has been an active research topic for decades, current state-of-the-art methods still suffer from instability or inaccuracy due to feature insufficiency or its inherent estimation drift, in many civilian environments. To resolve these issues, we propose a navigation system combing the SLAM and prior-map-based localization. Specifically, we consider additional integration of line and plane features, which are ubiquitous and more structurally salient in civilian environments, into the SLAM to ensure feature sufficiency and localization robustness. More importantly, we incorporate general prior map information into the SLAM to restrain its drift and improve the accuracy. To avoid rigorous association between prior information and local observations, we parameterize the prior knowledge as low dimensional structural priors defined as relative distances/angles between different geometric primitives. The localization is formulated as a graphbased optimization problem that contains sliding-window-based variables and factors, including IMU, heterogeneous features, and structure priors. We also derive the analytical expressions of Jacobians of different factors to avoid the automatic differentiation overhead. To further alleviate the computation burden of incorporating structural prior factors, a selection mechanism is adopted based on the so-called information gain to incorporate only the most effective structure priors in the graph optimization. Finally, the proposed framework is extensively tested on synthetic data, public datasets, and, more importantly, on the real UAV flight data obtained from a building inspection task. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.

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