Dense, Sonar-based Reconstruction of Underwater Scenes
Pedro V. Teixeira, Dehann Fourie, Michael Kaess, John J. Leonard
Abstract
Typically, the reconstruction problem is addressed in three independent steps: first, sensor processing techniques are used to filter and segment sensor data as required by the front end. Second, the front end builds the factor graph for the problem to obtain an accurate estimate of the robot’s full trajectory. Finally, the end product is obtained by further processing of sensor data, now re-projected from the optimized trajectory. In this paper we present an approach to model the reconstruction problem in a way that unifies the aforementioned problems under a single framework for a particular application: sonar-based inspection of underwater structures. This is achieved by formulating both the sonar segmentation and point cloud reconstruction problems as factor graphs, in tandem with the SLAM problem. We provide experimental results using data from a ship hull inspection test.
BibTeX
@inproceedings{iros2019_densesonarbasedr,
title = {Dense, Sonar-based Reconstruction of Underwater Scenes},
author = {Pedro V. Teixeira and Dehann Fourie and Michael Kaess and John J. Leonard},
booktitle = {IROS 2019},
year = {2019}
}