3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint

Kagami, Sho; Taira, Hajime; Miyashita, Naoyuki; Torii, Akihiko; Okutomi, Masatoshi

doi:10.1109/isie45063.2020.9152377

Cited by 13 publications

(21 citation statements)

References 36 publications

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“…Many VO algorithms for pipes leverage cylindrical information which exploits prior knowledge of the shape of the environment to improve pose estimation accuracy, and to help resolve scale ambiguity for monocular systems [196], [202], [204]. Robust methods have been developed that optimize the map points in the optimization by enforcing cylindrical regularity [139], [205]. Cylindrical regularity has also been used as prior knowledge for local pose optimization, to further improve accuracy [199].…”

Section: B Camerasmentioning

confidence: 99%

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

et al. 2021

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At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, selflocalising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot's position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve the multi-robot SLAM in the pipe environment.

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Section: B Camerasmentioning

confidence: 99%

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

et al. 2021

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“…Finally, they generated a 3D model of the pipe by mapping the texture to the triangular mesh. In [99], a method called Structure-from-Motion (SfM) system is used for mapping. SfM is a visionbased 3D reconstruction that reconstructs the 3D structure of pipe from sequential images for a pipe trajectory with an endoscope camera.…”

Section: A 3d Point Cloudmentioning

confidence: 99%

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

Kazeminasab¹,

Sadeghi

Janfaza

et al. 2021

IEEE Access

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“…Some methods [8,19] estimate the map point location by computing the intersection of the known cylindrical surface and the ray from the camera centre to the observation when the camera moves parallel with the pipe axis. Others methods [8,10] add cylindrical regularity to map points in the bundle adjustment (BA) algorithm to keep the points on the known cylindrical surface. Further, this prior knowledge has been used in local pose optimisation, which can optimise the camera poses and triangulate features iteratively [12].…”

Section: Related Workmentioning

confidence: 99%

“…Those assumptions do not typically hold in the SLAM problem. Cone detection among the triangular features has been used to lift restrictions on camera movement [10], which can be done without prior knowledge of the pipe axis, and is less sensitive to camera calibration [14]. This method detects multiple pipe instances with temporary map points incrementally per reconstructed model, which can not perform in real-time.…”

Section: Related Workmentioning

confidence: 99%

Improving SLAM in Pipe Networks by Leveraging Cylindrical Regularity

Zhang

Evans

Worley

et al. 2021

Towards Autonomous Robotic Systems

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Monocular visual Simultaneous Localisation and Mapping algorithms estimate map points and frame poses simultaneously based on video data. The estimated map point locations do not contain any structural information. Due to the measurement noise, the estimated trajectory is slightly different from the ground truth. This paper improves the estimation accuracy of trajectory in a pipe network by leveraging structural regularity. An optimisation-based method is used to detect a cylinder among map points in the SLAM back-end. When the cylinder is detected, the system enforces cylindrical regularity to the points from the cylindrical pipe surface, which is named cylindrical points. The estimated trajectory and map points will benefit from this structural information. This method is verified and evaluated on both synthetic data and realworld pipe video datasets.

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3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint

Cited by 13 publications

References 36 publications

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

Improving SLAM in Pipe Networks by Leveraging Cylindrical Regularity

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