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.

This work proposes the use of conditional Generative Adversarial Networks (cGANs) for acoustic-based 3D reconstruction. Acoustics being the most reliable sensor modality in underwater domains is accompanied with the loss of elevation angle in its images. The challenge of recovering the missing dimension in acoustic images have pushed researchers to try various methods and approaches over the past years. cGANs being an image-to-image translation method makes it possible to learn a desired style, and transforms the data from one modality to another. This was applied here as a way of transforming an acoustic image into another form which contains the elevation characteristics, such as depth images. Depth images are hard to acquire underwater, thus data was generated synthetically and used for training and testing the deep learning model. As a way of performance enhancement, real data was collected for training a Cycle-GAN network in the aim of transferring the realistic style into the synthetically generated images. Simulation experiments were conducted to evaluate the system and find out the best experimental setup, which was then used to carry out the real experiment. The system performed dense 3D reconstruction of the scanned object and proved to be applicable in real environments.

In this work we present a novel method for reconstructing 3D surfaces using a multi-beam imaging sonar. We integrate the intensities measured by the sonar from different viewpoints for fixed cell positions in a 3D grid. For each cell we integrate a feature vector that holds the mean intensity for a discretized range of viewpoints. Based on the feature vectors and independent sparse range measurements that act as ground truth information, we train convolutional neural networks that allow us to predict the signed distance and direction to the nearest surface for each cell. The predicted signed distances can be projected into a truncated signed distance field (TSDF) along the predicted directions. Utilizing the marching cubes algorithm, a polygon mesh can be rendered from the TSDF. Our method allows a dense 3D reconstruction from a limited set of viewpoints and was evaluated on three real-world datasets.

Learning the dynamics of robots from data can help achieve more accurate tracking controllers, or aid their navigation algorithms. However, when the actual dynamics of the robots change due to external conditions, on-line adaptation of their models is required to maintain high fidelity performance. In this work, a framework for on-line learning of robot dynamics is developed to adapt to such changes. The proposed framework employs an incremental support vector regression method to learn the model sequentially from data streams. In combination with the incremental learning, strategies for including and forgetting data are developed to obtain better generalization over the whole state space. The framework is tested in simulation and real experimental scenarios demonstrating its adaptation capabilities to changes in the robot's dynamics.

In this work we investigate the identification of a motion model for an autonomous underwater vehicle by applying different machine learning (ML) regression methods. By using the data collected from the robot's on-board navigation sensors, we train the regression models to learn the damping term which is regarded as one of the most uncertain components of the motion model. Four regression techniques are investigated namely, artificial neural networks, support vector machines, kernel ridge regression, and Gaussian processes regression. The performance of the identified models is tested through real experimental scenarios performed with the AUV Leng. The novelty of this work is the identification of an underwater vehicle's motion model, for the first time, through machine learning methods by using the robot's onboard sensory data. Results show that the damping model learned with nonlinear methods yield better estimates than the simplified linear and quadratic model which is identified with least-squares technique.

This paper presents an online technique which employs incremental support vector regression to learn the damping term of an underwater vehicle motion model, subject to dynamical changes in the vehicle's body. To learn the damping term, we use data collected from the robot's on-board navigation sensors and actuator encoders. We introduce a new sample-efficient methodology which accounts for adding new training samples, removing old samples, and outlier rejection. The proposed method is tested in a real-world experimental scenario to account for the model's dynamical changes due to a change in the vehicle's geometrical shape.