This paper presents an extension to visual inertial odometry (VIO) by introducing tightly-coupled fusion of magnetometer measurements. A sliding window of keyframes is optimized by minimizing re-projection errors, relative inertial errors, and relative magnetometer orientation errors. The results of IMU orientation propagation are used to efficiently transform magnetometer measurements between frames producing relative orientation constraints between consecutive frames. The soft and hard iron effects are calibrated using an ellipsoid fitting algorithm. The introduction of magnetometer data results in significant reductions in the orientation error and also in recovery of the true yaw orientation with respect to the magnetic north. The proposed framework operates in all environments with slow-varying magnetic fields, mainly outdoors and underwater. We have focused our work on the underwater domain, especially in underwater caves, as the narrow passage and turbulent flow make it difficult to perform loop closures and reset the localization drift. The underwater caves present challenges to VIO due to the absence of ambient light and the confined nature of the environment, while also being a crucial source of fresh water and providing valuable historical records. Experimental results from underwater caves demonstrate the improvements in accuracy and robustness introduced by the proposed VIO extension.

This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. Underwater robots operating in a challenging environment are required to know their pose at all times. All vision-based localization schemes are prone to failure due to poor visibility conditions, color loss, and lack of features. The proposed approach utilizes a model of the robot's kinematics together with proprioceptive sensors to maintain the pose estimate during visual-inertial odometry (VIO) failures. Furthermore, the trajectories from successful VIO and the ones from the model-driven odometry are integrated in a coherent set that maintains a consistent pose at all times. Health-monitoring tracks the VIO process ensuring timely switches between the two estimators. Finally, loop closure is implemented on the overall trajectory. The resulting framework is a robust estimator switching between model-based and visual-inertial odometry (SM/VIO). Experimental results from numerous deployments of the Aqua2 vehicle demonstrate the robustness of our approach over coral reefs and a shipwreck.

This paper addresses real-time dense 3D reconstruction for a resource-constrained Autonomous Underwater Vehicle (AUV). Underwater vision-guided operations are among the most challenging as they combine 3D motion in the presence of external forces, limited visibility, and absence of global positioning. Obstacle avoidance and effective path planning require online dense reconstructions of the environment. Autonomous operation is central to environmental monitoring, marine archaeology, resource utilization, and underwater cave exploration. To address this problem, we propose to use SVIn2, a robust VIO method, together with a real-time 3D reconstruction pipeline. We provide extensive evaluation on four challenging underwater datasets. Our pipeline produces comparable reconstruction with that of COLMAP, the state-of-the-art offline 3D reconstruction method, at high frame rates on a single CPU.

In this paper we present a complete framework for Underwater SLAM utilizing a single inexpensive sensor. Over the recent years, imaging technology of action cameras is producing stunning results even under the challenging conditions of the underwater domain. The GoPro 9 camera provides high definition video in synchronization with an Inertial Measurement Unit (IMU) data stream encoded in a single mp4 file. The visual inertial SLAM framework is augmented to adjust the map after each loop closure. Data collected at an artificial wreck of the coast of South Carolina and in caverns and caves in Florida demonstrate the robustness of the proposed approach in a variety of conditions.

In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves in a communication-constrained underwater environment is essential for many applications such as underwater exploration, mapping, multi-robot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image-to-image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSAC-based PnP. Experimental results in real-world underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness and accuracy of the proposed technique in terms of translation error and orientation error over the state-of-the-art methods. The code is publicly available.

A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct and indirect methods that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

The primary purpose of the current paper is to design a fast and accurate performance model framework for exploring various thread-to-core mapping strategies (MS) and estimating steady state cycles per instruction (CPI). It is directed towards efficiently exploring these performance metrics for large parallel applications for shared memory multicores. This work establishes a hybrid Markov Chain Model (MCM) and Model Tree (MT) based system-level performance prediction model framework. The model is validated with an Electromagnetics application for 12 different mapping strategies. The average performance prediction error is 0.168% with standard deviation of 3.866%. The total run time of model is of the order of minutes, whereas the actual application execution time is in terms of several days.

We demonstrate a novel cross-stack control theoretic approach in designing a predictive controller that can automatically track changes in the multimedia workload to maintain a desired metric of application quality while minimizing power consumption.

Exploring the vast microarchitectural design space of chip multiprocessors (CMPs) through the traditional approach of exhaustive simulations is impractical due to the long simulation times and its super-linear increase with core scaling. Kernel based statistical machine learning algorithms can potentially help predict multiple performance metrics with non-linear dependence on the CMP design parameters. In this paper, we describe and evaluate a machine learning framework that uses Kernel Canonical Correlation Analysis (KCCA) to predict the power dissipation and performance of CMPs. Specifically we focus on modeling the microarchitecture of a highly multithreaded CMP targeted towards packet processing. We use a cycle accurate CMP simulator to generate training samples required to build the model. Despite sampling only 0.016% of the design space we observe a median error of 6-10% in the KCCA predicted processor power dissipation and performance.

The computationally intensive power flow problem determines the voltage magnitude and phase angle at each bus in a power system for hundreds of thousands of buses under balanced three-phase steady-state conditions. We report an FPGA acceleration of the Gauss-Seidel based power flow solver employed in the transmission module of the GridLAB-D power distribution simulator and analysis tool. The prototype hardware is implemented on an SGI Altix-RASC system equipped with a Xilinx Virtex-II 6000 FPGA. Due to capacity limitations of the FPGA, only the bus voltage calculations of the power network are implemented on hardware while the branch current calculations are implemented in software. For a 200,000 bus system, the bus voltage calculation on the FPGA achieves a 48x speed-up with PQ buses and a 62x for PV over an equivalent sequential software implementation. The average overall speed up of the CPU-FPGA implementation with 100 iterations of the Gauss-Seidel power solver is 2.6x over a software implementation, with the branch calculations on the CPU accounting for 85% of the total execution time. The CPU-FPGA implementation also shows linear scaling with increase in the size of the input power network.

Microfluidics-based biochips consist of microfluidic arrays on rigid substrates through which movement of fluids is tightly controlled to facilitate biological reactions. Biochips are soon expected to revolutionize biosensing, clinical diagnostics, environmental monitoring, and drug discovery. Critical to the deployment of the biochips in such diverse areas is the dependability of these systems. Thus robust testing and diagnosis techniques are required to ensure adequate level of system dependability. Due to the underlying mixed technology and mixed energy domains, such biochips exhibit unique failure mechanisms and defects. In this article efficient parallel testing and diagnosis algorithms are presented that can detect and locate single as well as multiple faults in a microfluidic array without flooding the array, a problem that has hampered realistic implementation of several existing strategies. The fault diagnosis algorithms are well suited for built-in self-test that could drastically reduce the operating cost of microfluidic biochip. Also, the proposed alogirthms can be used both for testing and fault diagnosis during field operation as well as increasing yield during the manufacturing phase of the biochip. Furthermore, these algorithms can be applied to both online and offline testing and diagnosis. Analytical results suggest that these strategies that can be used to design highly dependable biochip systems.

Microfluidics-based biochips consist of microfluidic arrays on rigid substrates through which, movement of fluids is tightly controlled to facilitate biological reactions. Biochips are soon expected to revolutionize biosensing, clinical diagnostics, and drug discovery. Critical to the deployment of biochips in such diverse areas is the dependability of these systems. Thus, robust testing techniques are required to ensure an adequate level of system dependability. Due to the underlying mixed technology and energy domains, such biochips exhibit unique failure mechanisms and defects. In this article we present a highly effective fault diagnosis strategy that uses a single source and sink to detect and locate multiple faults in a microfluidic array, without flooding the array, a problem that has hampered realistic implementations of all existing strategies. The strategy renders itself well for a built-in self-test that could drastically reduce the operating cost of microfluidic biochips. It can be used during both the manufacturing phase of the biochip, as well as field operation. Furthermore, the algorithm can pinpoint the actual fault, as opposed to merely the faulty regions that are typically identified by strategies proposed in the literature. Also, analytical results suggest that it is an effective strategy that can be used to design highly dependable biochip systems.

In general, a load balancing algorithm improves a system performance. Obviously, larger the difference between the task arrival rates at various processors, more the system is imbalanced and more improvement in the system performance is achieved using a load balancing algorithm. The existing works which have used an experimental technique to show the improvement in the system performance under a load balancing algorithm have used an ad hoc procedure to select the task arrival rates for various processors. Thus, their experimental results necessarily may not provide a complete picture of the improvement in the system performance under their load balancing algorithms. The authors present a systematic scheme for the selection of the task arrival rates at various processors such that experimental results reflect a complete picture of the improvement in the system performance under a load balancing algorithm. The idea has been motivated by the well-known Taguchi technique used in quality control.>