Paul Szenher

Ivana Collado-Gonzalez, John McConnell, Paul Szenher et al.

Scene reconstruction is an essential capability for underwater robots navigating in close proximity to structures. Monocular vision-based reconstruction methods are unreliable in turbid waters and lack depth scale information. Sonars are robust to turbid water and non-uniform lighting conditions, however, they have low resolution and elevation ambiguity. This work proposes a real-time opti-acoustic scene reconstruction method that is specially optimized to work in turbid water. Our strategy avoids having to identify point features in visual data and instead identifies regions of interest in the data. We then match relevant regions in the image to corresponding sonar data. A reconstruction is obtained by leveraging range data from the sonar and elevation data from the camera image. Experimental comparisons against other vision-based and sonar-based approaches at varying turbidity levels, and field tests conducted in marina environments, validate the effectiveness of the proposed approach. We have made our code open-source to facilitate reproducibility and encourage community engagement.

John McConnell, Armon Shariati, Paul Szenher et al.

Multi-robot simultaneous localization and mapping (SLAM) is a fundamental task in multi-robot operations. Robots must have a common understanding of their location and that of their team members to complete coordinated actions. However, multi-robot SLAM between Uncrewed Surface Vessels (USVs) and Autonomous Underwater Vehicles (AUVs) has primarily been achieved through acoustic pinging between robots to retrieve range measurements; a measurement technique requires that robots to be in similar locations simultaneously, have an uninterrupted path for signal propagation, and may necessitate synchronized clocks. This is especially challenging in complex, cluttered maritime environments, where structures may impede signals. However, these same structures may be observable above and below the water's surface, presenting an opportunity for inter-robot SLAM loop closure between USV and AUV data streams. This work builds upon recent research on inter-robot SLAM loop closure between USV and AUV data, extending it to propose a centralized multi-robot SLAM system. Each robot performs its state estimation, and we detect loop closures between each AUV and the USV data. These inter-robot loop closures are used to merge each robot's state estimate into a centralized graph, yielding estimates for the whole time history of the USV and all AUVs in the system. Validation is performed using real-world perceptual data in three different environments. Results show improved errors for AUVs in the multi-robot SLAM system compared to single-robot SLAM over the same trajectories. To our knowledge, this is the first instance of a multi-robot SLAM system with AUVs and USVs built on loop closures rather than acoustic distance measurements.

Fanfei Chen, Paul Szenher, Yewei Huang et al.

This paper studies the problem of autonomous exploration under localization uncertainty for a mobile robot with 3D range sensing. We present a framework for self-learning a high-performance exploration policy in a single simulation environment, and transferring it to other environments, which may be physical or virtual. Recent work in transfer learning achieves encouraging performance by domain adaptation and domain randomization to expose an agent to scenarios that fill the inherent gaps in sim2sim and sim2real approaches. However, it is inefficient to train an agent in environments with randomized conditions to learn the important features of its current state. An agent can use domain knowledge provided by human experts to learn efficiently. We propose a novel approach that uses graph neural networks in conjunction with deep reinforcement learning, enabling decision-making over graphs containing relevant exploration information provided by human experts to predict a robot's optimal sensing action in belief space. The policy, which is trained only in a single simulation environment, offers a real-time, scalable, and transferable decision-making strategy, resulting in zero-shot transfer to other simulation environments and even real-world environments.

Tixiao Shan, Jinkun Wang, Fanfei Chen et al.

We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the point cloud captured by a sparse 3D lidar, we convert this problem from 3D Euclidean space into an image super-resolution problem in 2D image space, which is solved using a deep convolutional neural network. By projecting a point cloud onto a range image, we are able to efficiently enhance the resolution of such an image using a deep neural network. Typically, the training of a deep neural network requires vast real-world data. Our approach does not require any real-world data, as we train the network purely using computer-generated data. Thus our method is applicable to the enhancement of any type of 3D lidar theoretically. By novelly applying Monte-Carlo dropout in the network and removing the predictions with high uncertainty, our method produces high accuracy point clouds comparable with the observations of a real high resolution lidar. We present experimental results applying our method to several simulated and real-world datasets. We argue for the method's potential benefits in real-world robotics applications such as occupancy mapping and terrain modeling.