Hough$^2$Map – Iterative Event-Based Hough Transform for High-Speed Railway Mapping

Tschopp, Florian; Cornelius, von Einem,; Cramariuc, Andrei; Hug, David; Palmer, A. W.; Siegwart, Roland; Chli, Margarita; Nieto, Juan

doi:10.1109/lra.2021.3061404

Cited by 19 publications

(10 citation statements)

References 45 publications

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“…Correctness Instance Persistence Hough transf. [9], [10], [11] + -+ Non parametric [12], [13] + --Spatio-temporal [14], [15] + + -Ours + + + and frequent changes in appearance in the stream of the events.…”

Section: Methods Categorymentioning

confidence: 99%

“…The difficulty of line tracking using events led previous works [9], [10], [11], [12], [13], [14], [15] to explore different techniques but pursuing a common goal: robustness. We identify the robustness of tracking a line as the combination of three desired characteristics.…”

Section: Methods Categorymentioning

confidence: 99%

“…The work in [10] proposes an application of a Hough transform based tracker for agile aerial robot maneuvering. Consecutive Hough transform based detectors were used in [11] to detect and track poles in a railway localization system. Although Hough transform based methods have been used for events, they do not fully leverage the asynchronous nature of event cameras.…”

Section: Related Workmentioning

confidence: 99%

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Powerline Tracking with Event Cameras

Dietsche

Cioffi

Hidalgo-Carrió

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Autonomous inspection of powerlines with quadrotors is challenging. Flights require persistent perception to keep a close look at the lines. We propose a method that uses event cameras to robustly track powerlines. Event cameras are inherently robust to motion blur, have low latency, and high dynamic range. Such properties are advantageous for autonomous inspection of powerlines with drones, where fast motions and challenging illumination conditions are ordinary. Our method identifies lines in the stream of events by detecting planes in the spatio-temporal signal, and tracks them through time. The implementation runs onboard and is capable of detecting multiple distinct lines in real time with rates of up to 320 thousand events per second. The performance is evaluated in real-world flights along a powerline. The tracker is able to persistently track the powerlines, with a mean lifetime of the line 10× longer than existing approaches.

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Section: Methods Categorymentioning

confidence: 99%

Section: Methods Categorymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Powerline Tracking with Event Cameras

Dietsche

Cioffi

Hidalgo-Carrió

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Third, it is significantly faster than raster scanning in detecting ellipse-enclosing characters in largesized document images [17]. Fourth, it is capable of detecting nearby structures to map robust relevant landmarks for highspeed railway mapping [18]. Lastly, it can be used to detect and track road lanes for lane departure warning systems [19].…”

Section: A Line-based Methodsmentioning

confidence: 99%

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

et al. 2021

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Power outages can disrupt daily domestic activities as well as the economy as operations are hampered when they occur. They can decrease work productivity by delaying operations that require electricity. The key solution to this problem is to ensure that there are fewer or no power interruptions. This can be achieved by ensuring secure and continuous network operations through regular maintenance and inspection. However, the traditional inspection technique of foot patrol is risky, laborious, and timeconsuming. A preferable contemporary technique uses an unmanned aerial vehicle (UAV) for inspecting distribution lines. Detecting power lines are crucial for real-time motion planning and navigation of UAVs. Previous techniques that depend on conventional filters and gradients may fail to detect power lines because of noisy backgrounds. Thus, this study proposes a novel technique by adopting the Transfer Learning approach The process involves fine-tuning a pretrained network from Point Instance Network (PINet) road lane detection model to fit the power line detection task. The proposed method extends the PINet model by adding a comparator for rotation block before it and a postprocessing block after it. This study generates four versions of the model, each of which was trained on one of the following datasets (i) self-gathered images captured by a handheld camera, (ii) a drone, (iii) publicly accessible images from the Power Line Dataset of Mountain Scene (PLDM), and (iv) Power Line Dataset of Urban Scene (PLDU). Experimental results on each dataset confirm the feasibility of the proposed approach.

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“…Semantic objects can be mathematically represented in many different ways, typically as a trade-off between parametrization complexity and representation strength. Simple 3D point representations as mapped in [28], [30] consist of three position parameters, equivalent to standard keypoints. This benefits from an easy integration of well-known re-projection cost functions, but especially objects with larger sizes or non-spherical shapes cannot be represented accurately.…”

Section: Related Workmentioning

confidence: 99%

Superquadric Object Representation for Optimization-based Semantic SLAM

Tschopp,

Nieto,

Siegwart

et al. 2021

Preprint

Self Cite

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Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant viewpoint and appearance changes. However, how semantic objects should be represented for an efficient inclusion in optimizationbased SLAM frameworks is still an open question. Superquadrics (SQs) are an efficient and compact object representation, able to represent most common object types to a high degree, and typically retrieved from 3D point-cloud data. However, accurate 3D point-cloud data might not be available in all applications. Recent advancements in machine learning enabled robust object recognition and semantic mask measurements from camera images under many different appearance conditions. We propose a pipeline to leverage such semantic mask measurements to fit SQ parameters to multi-view camera observations using a multi-stage initialization and optimization procedure. We demonstrate the system's ability to retrieve randomly generated SQ parameters from multi-view mask observations in preliminary simulation experiments and evaluate different initialization stages and cost functions.

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Hough$^2$Map – Iterative Event-Based Hough Transform for High-Speed Railway Mapping

Cited by 19 publications

References 45 publications

Powerline Tracking with Event Cameras

Powerline Tracking with Event Cameras

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

Superquadric Object Representation for Optimization-based Semantic SLAM

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