May 15, 2024, 4:46 a.m. | Samiran Gode, Akshay Hinduja, Michael Kaess

cs.CV updates on

arXiv:2310.15023v2 Announce Type: replace
Abstract: In this paper, we address the challenging problem of data association for underwater SLAM through a novel method for sonar image correspondence using learned features. We introduce SONIC (SONar Image Correspondence), a pose-supervised network designed to yield robust feature correspondence capable of withstanding viewpoint variations. The inherent complexity of the underwater environment stems from the dynamic and frequently limited visibility conditions, restricting vision to a few meters of often featureless expanses. This makes camera-based systems …

abstract arxiv association data feature features image imaging network novel paper replace robust slam sonar sonic supervised learning through type underwater

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Asset Information Manager (AIM) (m/w/d) / Facility Information Manager (m/w/d)

@ Covestro | Leverkusen