SAVOR: Sonar-Aided Visual Odometry and Reconstruction for Autonomous Underwater Vehicles^*

Abstract

Visual odometry (VO) relies on sequential camera images to estimate robot motion. For underwater robots, this is often complicated by turbidity, light attenuation, and environments containing scarce or repetitive features. Even ideal imagery suffers from the issue of scale ambiguity common to all monocular VO implementations. To address these issues, we supplement a camera with a multibeam echosounder. This acoustic, time-of-flight sensor comes with its own challenges, including relatively slow and sparse measurements that can be further degraded by backscatter from suspended particulate matter as well as interfering sounds from nearby marine traffic. We propose a method for fusing only data from these two inspection sensors into a hybrid VO solution that does not rely on IMU, DVL, or any other positioning sensor. We demonstrate this method on real data collected by an autonomous underwater vehicle performing end-to-end pipeline inspection in the open ocean, where multiple passes through the same scene (i.e., the “loop closure” common to SLAM algorithms) is often time and cost prohibitive. We also show how this approach can be extended for the creation of dense point clouds that provide a colored reconstruction of the surveyed scene.