Driving Datasets Literature Review

Laflamme, Charles-Éric Noël; Pomerleau, François; Giguère, Philippe

doi:10.48550/arxiv.1910.11968

Cited by 1 publication

(1 citation statement)

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“…Moreover, currently datasets have become an important tool in the scientific and industrial communities to implement, validate and benchmark various techniques in multiple areas such as image analysis and sensor fusion [21]. This provides a major boost in the exploration of new techniques for maritime robotics with works for depth completion [22], [23], object recognition [24] and classification [25], obstacle detection [26] and the expansion of sensor capabilities [27].…”

Section: Related Workmentioning

confidence: 99%

Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

2022

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A growing interest in ocean exploration for scientific and commercial research has been shown, mainly due to the technological developments for maritime and offshore industries. The use of Autonomous surface vehicles (ASV) have a promising role to revolutionize the transportation, monitorization, operation and maintenance areas, allowing to perform distinct task from offshore assets inspection to harbor patrolling. This work presents SENSE, an autonomouS vEssel for multi-domaiN inSpection and maintEnance. It provides an open-source hardware and software architecture that is easy to replicate for both research institutes and industry. This is a multi-purpose vehicle capable of acquiring multi-domain data for inspecting and reconstructing maritime infrastructures. SENSE provides a research platform which can increase the situational awareness capabilities for ASVs. SENSE full configuration provides multimodal sensory data acquired from both domains using a Light Detection And Ranging (LiDAR), a stereoscopic camera, and a multibeam echosounder. In addition, it supplies navigation information obtained from a real-time kinematic satellite navigation system and inertial measurement units. Moreover, the tests performed at the harbor of Marina de Leça, at Porto, Portugal, resulted in a dataset which captures a fully operational harbor. It illustrates several conditions on maritime scenarios, such as undocking and docking examples, crossings with other vehicles and distinct types of moored vessels. The data available represents both domains of the maritime scenario, being the first public dataset acquired for multi-domain observation using a single vehicle. This paper also provides examples of applications for navigation and inspection on multi-domain scenarios, such as odometry estimation, bathymetric surveying and multi-domain mapping.INDEX TERMS Maritime robotics, 3D perception, heterogeneous sensors, maritime dataset, multi-domain data.

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