This paper proposes a novel decision-making framework for planning "when" and "where" to deploy robots based on prior data with the goal of persistently monitoring a spatio-temporal phenomenon in an environment. We specifically focus on large lake monitoring, where remote sensors, such as satellites, can provide a snapshot of the target phenomenon at regular cycles. Between these cycles, Autonomous Surface Vehicles (ASVs) can be deployed to maintain an up-to-date model of the phenomenon. However, deploying ASVs has a significant logistical overhead in terms of time and cost. It requires a team of people to go on site and spend typically a day to monitor the deployment. It is vital to not only be intentional about where to sample in the environment on a given day, but also determine the worth of deploying the ASVs that day at all. Therefore, we propose a persistent monitoring strategy that provides the days and locations of when and where to sample with the robots by leveraging Gaussian Process model estimates of future trends based on collected remote sensing and point measurement data. Our approach minimizes the number of days and locations for sampling, while preserving the quality of estimates. Through simulation experiments using realistic spatio-temporal datasets, we demonstrate the benefits of our approach over traditional deployment strategies, including significant savings on the effort and operational cost of deploying the ASVs.

The optical effects that are observed in underwater imagery are more complex than those in-air. This is partially because we enclose most underwater cameras in a watertight enclosure, such as a hemispheric dome window. We then observe optical issues including the distortion effects of the lens, e.g., wide-angle field-of-view (FOV), the refractive effects at the enclosure (water-acrylic and acrylic-air) interfaces, and offset effects of a non-centered camera with respect to the dome. In this paper, we present an N-Sphere (NS) and Shifted N-Sphere (S-NS) camera models, tailored to these cameras and lenses mounted in water-tight dome enclosures. The proposed camera models treat each layer of effects as a ‘sphere’ that a 3D point will project on. Furthermore, the S-NS model includes additional parameters to address the camera offset variability. The versatility of the NS model makes it applicable to various lenses, as validated with fisheye (FOV >120°) and wide-FOV (FOV ≈ 120°). We validated our models with different in-water calibration sequences, lenses, and housing setups, as well as with comparisons with other state-of-the-art camera models. Additionally, we demonstrated the performance of our proposed models in an example stereo-based visual odometry application. The low computational load of the proposed models makes it ideal for integrating in real-time visual navigation and reconstruction frameworks. We provide full math derivations of the proposed models as well as example C++ header files1for easy incorporation in independent projects.

Underwater depth estimation is essential for safe Autonomous Underwater Vehicles (AUV) navigation. While there has been recent advances in out-of-water monocular depth estimation, it is difficult to apply these methods to the underwater domain due to the lack of well-established datasets with labelled ground truths. In this paper, we propose a novel method for self-supervised underwater monocular depth estimation by leveraging a low-cost single-beam echosounder (SBES). We also present a synthetic dataset for underwater depth estimation to facilitate visual learning research in the underwater domain, available at https://github.com/hdacnw/sbes-depth. We evaluated our method on the proposed dataset with results outperforming previous methods and tested our method in a dataset we collected with an inexpensive AUV. We further investigated the use of SBES as an additional component in our self-supervised method for up-to-scale depth estimation providing insights on next research directions.

This paper proposes a novel underwater Multi-View Photometric Stereo (MVPS) framework for reconstructing scenes in 3-D with a non-stationary low-cost robot equipped with a monocular camera and fixed lights. The underwater realm is the primary focus of study here, due to the challenges in utilizing underwater camera imagery and lack of low-cost reliable localization systems. Previous underwater PS approaches provided accurate scene reconstruction results, but assumed that the robot was stationary at the bottom. This assumption is limiting, as many artifacts, reefs, and man-made structures are large and meters above the bottom. Our proposed MVPS framework relaxes the stationarity assumption by utilizing a monocular SLAM system to estimate small robot motions and extract an initial sparse feature map. To compensate for the scale inconsistency in monocular SLAM output, our MVPS optimization scheme collectively estimates a high-quality, dense 3-D reconstruction and corrects the camera pose estimates. We also present an attenuation and camera-light extrinsic parameter calibration method for non-stationary robots. Finally, validation experiments with a BlueROV2 demonstrated the low-cost capability of producing high-quality scene reconstructions. Overall, this work is the foundation of an active perception pipeline for robots (i.e., underwater, ground, and aerial) to explore and map complex structures in high accuracy and resolution with an inexpensive sensor-light configuration.

This paper proposes a methodology for real-time depth estimation of underwater monocular camera images, fusing measurements from a single-beam echosounder. Our system exploits the echosounder's detection cone to match its measurements with the detected feature points from a monocular SLAM system. Such measurements are integrated in a monocular SLAM system to adjust the visible map points and the scale. We also provide a novel calibration process to determine the extrinsic between camera and echosounder to have reliable matching. Our proposed approach is implemented within ORB-SLAM2 and evaluated in a swimming pool and in the ocean to validate image depth estimation improvement. In addition, we demonstrate its applicability for improved underwater color correction. Overall, the proposed sensor fusion system enables inexpensive underwater robots with a monocular camera and echosounder to correct the depth estimation and scale in visual SLAM, leading to interesting future applications, such as underwater exploration and mapping.

Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic-, physic-, and learning-based color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.