Research on the underwater laser fuze target detecting technique

Shu-guang, Pan; Deng, Jiahao; Su-juan, Wei; Sun, Dongning

doi:10.1109/iccea.2004.1459364

Cited by 6 publications

(2 citation statements)

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“…The aforementioned factors have motivated the development of expensive hardware in the hope of obtaining a cleaner and more reliable two-dimensional (2D) and 3D input. Common perception sensors underwater are light detection and ranging (LiDAR), sound navigation ranging (Sonar) and customised stereo cameras [2], [3], [13]. While the hardware for underwater perception has seen great progress in the last two decades [13], methodologies for underwater object pose estimation are still an open research topic.…”

Section: Related Workmentioning

confidence: 99%

“…Out of 10 different poses, inside the arm's working space, 6 resulted in accurate poses that our arm could successfully reach. The failed trials are attributed to 3 Experiments:https://www.youtube.com/watch?v=xPAbxwh5JGM flickering in the AAE pose estimation. These oscillations are the result of unavoidable reflections from the water surface due to the shallowness of our testing environment.…”

Section: Underwater Manipulation Use Casementioning

confidence: 99%

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Model-Based Underwater 6D Pose Estimation from RGB

Sapienza¹,

Govi²,

Aldhaheri³

et al. 2023

Preprint

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Object pose estimation underwater allows an autonomous system to perform tracking and intervention tasks. Nonetheless, underwater target pose estimation is remarkably challenging due to, among many factors, limited visibility, light scattering, cluttered environments, and constantly varying water conditions. An approach is to employ sonar or laser sensing to acquire 3D data, but besides being costly, the resulting data is normally noisy. For this reason, the community has focused on extracting pose estimates from RGB input. However, the literature is scarce and exhibits low detection accuracy. In this work, we propose an approach consisting of a 2D object detection and a 6D pose estimation that reliably obtains object poses in different underwater scenarios. To test our pipeline, we collect and make available a dataset of 4 objects in 10 different real scenes with annotations for object detection and pose estimation. We test our proposal in real and synthetic settings and compare its performance with similar end-to-end methodologies for 6D object pose estimation. Our dataset contains some challenging objects with symmetrical shapes and poor texture. Regardless of such object characteristics, our proposed method outperforms stateof-the-art pose accuracy by ∼ 8%. We finally demonstrate the reliability of our pose estimation pipeline by doing experiments with an underwater manipulation in a reaching task.

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