Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod P.5
Authors: DeBortoli, Robert; Nicolai, Austin; Li, Fuxin; Hollinger, Geoffrey
Title: Real-time Underwater 3D Reconstruction Using Global Context and Active Labeling
Abstract:
In this work we develop a novel framework that enables the real-time 3D reconstruction of underwater environments using features from 2D sonar images. Due to noisy and low-resolution imagery as compared with standard cameras, automatic feature extractors for sonar images are not reliable in many scenarios. Thus, a human often needs to hand-select features in sonar imagery for environment reconstructions. Given the high data capture rates of standard imaging sonars (on the order of 20Hz), hand-annotating the features in every frame cannot be done in real-time. To address this we use a Convolutional Neural Network (CNN) that analyzes incoming imagery in real-time and proposes only a small subset of high-quality frames to the user for feature annotation. We demonstrate that our approach provides real-time reconstruction capability without loss in classification performance on datasets captured onboard our underwater vehicle while operating in a variety of environments.
ICRA 2018 Spotlight Video
Interactive Session Thu AM Pod P.5
Authors: DeBortoli, Robert; Nicolai, Austin; Li, Fuxin; Hollinger, Geoffrey
Title: Real-time Underwater 3D Reconstruction Using Global Context and Active Labeling
Abstract:
In this work we develop a novel framework that enables the real-time 3D reconstruction of underwater environments using features from 2D sonar images. Due to noisy and low-resolution imagery as compared with standard cameras, automatic feature extractors for sonar images are not reliable in many scenarios. Thus, a human often needs to hand-select features in sonar imagery for environment reconstructions. Given the high data capture rates of standard imaging sonars (on the order of 20Hz), hand-annotating the features in every frame cannot be done in real-time. To address this we use a Convolutional Neural Network (CNN) that analyzes incoming imagery in real-time and proposes only a small subset of high-quality frames to the user for feature annotation. We demonstrate that our approach provides real-time reconstruction capability without loss in classification performance on datasets captured onboard our underwater vehicle while operating in a variety of environments.
