TY - JOUR
T1 - Overhead Image Factors for Underwater Sonar-Based SLAM
AU - McConnell, John
AU - Chen, Fanfei
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globally-referenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. We then use this synthetic overhead image to register our observations to the provided global overhead image. Once registered, the transformation is introduced as a factor into a pose SLAM factor graph. We use a state-of-the-art simulation environment to perform validation over a series of benchmark trajectories and quantitatively show the improved accuracy of robot state estimation using the proposed approach. We also show qualitative outcomes from a real AUV field deployment. datasets, quantitatively demonstrating its accuracy, stability, and data-efficiency.
AB - Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globally-referenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. We then use this synthetic overhead image to register our observations to the provided global overhead image. Once registered, the transformation is introduced as a factor into a pose SLAM factor graph. We use a state-of-the-art simulation environment to perform validation over a series of benchmark trajectories and quantitatively show the improved accuracy of robot state estimation using the proposed approach. We also show qualitative outcomes from a real AUV field deployment. datasets, quantitatively demonstrating its accuracy, stability, and data-efficiency.
KW - Marine robotics
KW - SLAM
KW - range sensing
UR - http://www.scopus.com/inward/record.url?scp=85125703321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125703321&partnerID=8YFLogxK
U2 - 10.1109/LRA.2022.3154048
DO - 10.1109/LRA.2022.3154048
M3 - Article
AN - SCOPUS:85125703321
VL - 7
SP - 4901
EP - 4908
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 2
ER -