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We propose a framework for tightly-coupled lidarvisual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is composed of two sub-systems: a visual-inertial system (VIS) and a lidar-inertial system (LIS). The two subsystems are designed in a tightly-coupled manner, in which the VIS leverages LIS estimation to facilitate initialization. The accuracy of the VIS is improved by extracting depth information for visual features using lidar measurements. In turn, the LIS utilizes VIS estimation for initial guesses to support scan-matching. Loop closures are first identified by the VIS and further refined by the LIS. LVI-SAM can also function when one of the two sub-systems fails, which increases its robustness in both texture-less and feature-less environments. LVI-SAM is extensively evaluated on datasets gathered from several platforms over a variety of scales and environments. Our implementation is available at https://git.io/lvi-sam.

Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closed-loop control relative to features such as weld-lines and biofouling. In the complex area, we implemented new large-scale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent operations on naval ships.

In this paper we address the problem of driftfree navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach only uses onboard sensors in a simultaneous localization and mapping setting and removes the need for any external infrastructure like acoustic beacons. We extract dense features from a forwardlooking imaging sonar and apply pair-wise registration between sonar frames. The registrations are combined with onboard velocity, attitude and acceleration sensors to obtain an improved estimate of the vehicle trajectory. We show results from several experiments that demonstrate drift-free navigation in various underwater environments.

Abstract-We present a hybrid algorithm that plans feasible paths for 100% sensor coverage of complex 3D structures. The structures to be inspected are segmented to isolate planar areas, and back-and-forth sweep paths are generated to view as much of these planar areas as possible while avoiding collision. A randomized planning procedure fills in the remaining gaps in coverage. The problem of selecting an order to traverse the elements of the inspection is solved by reduction to the traveling salesman problem. We present results of the planning algorithm for an autonomous underwater vehicle inspecting the in-water portion of a ship hull. The randomized configurations succeed in observing confined and occluded areas, while the 2D sweep paths succeed in covering the open areas. I. INTRODUCTIONCoverage path planning enables fast and efficient task completion in applications that require an autonomous agent to sweep an end effector over some portion of its workspace, including sensing, cleaning, painting, and plowing [7]. Optimal coverage paths often utilize a back-and-forth sweeping motion to cover the required areas efficiently. This is achieved in obstacle-filled 2D workspaces using cell decomposition methods [6], [14], which allow areas of open floorspace to be swept with uninterrupted motions. In 3D workspaces, the coverage task typically requires a full sweep of the interior or exterior boundary of a 3D structure embedded in the workspace. Back-and-forth sweeping has achieved uniform coverage of curved surface patches [3], and circumferential looping around 2D cross-sections has been used to cover the full boundary of closed 3D structures [2], [5].The paths planned by these algorithms contain uniform spacing between tracklines and often accumulate data sliceby-slice along a single spatial dimension of the workspace. Travel along a highly regular inspection route of this type allows a human operator to monitor task completion, and facilitates easy reading and interpretation of a sensor-based data product. To our knowledge, however, the existence of an arbitratry, collision-free coverage path is not a sufficient condition for the existence of a route with uniform spacing or a layout along a single spatial dimension.On the other hand, covering the boundary of a 3D structure using randomly sampled view configurations, a technique that employs a discrete set of stationary views rather than a continuous sensing trajectory, [9], [10], has been shown

We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). Unlike a large body of prior work, we focus on planning the views of the AUV to improve the quality of the inspection, rather than maximizing the accuracy of a given data stream. We formulate the inspection planning problem as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We rigorously analyze the benefit of adaptive re-planning for such problems, and we prove that the potential benefit of adaptivity can be reduced from exponential to a constant factor by changing the problem from cost minimization with a constraint on information gain to variance reduction with a constraint on cost. Such analysis allows the use of robust, nonadaptive planning algorithms that perform competitively with adaptive algorithms. Based on our analysis, we propose a method for constructing 3D meshes from sonar-derived point clouds, and we introduce uncertainty modeling through non-parametric Bayesian regression. Finally, we demonstrate the benefit of active inspection planning using sonar data from ship hull inspections with the Bluefin-MIT Hovering AUV.

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