Sharad Nagappa

Real-time mosaicing with two-dimensional forward-looking sonar

Forward-looking sonar can be used for underwater mapping when water visibility is poor. The generation of an acoustic mosaic of the environment is of high interest when underwater vehicles are used for surveys or search tasks. Moreover, if the mosaic is generated in real-time it can be used to provide instantaneous location feedback (e.g. to a ROV pilot or to an AUV), to ensure complete coverage of an area or facilitate the immediate location of targets. In this paper, we present an approach for achieving such a real-time mosaicing capability. Our system considers a simplified imaging model and estimates 2D sonar motions from the pairwise registration of sonar frames. The registration is performed by using a Fourier-based technique, avoiding the extraction of features and ensuring a fast implementation. The mosaicing problem is formulated using a pose graph, with the vertices being the sonar image positions and the edges being constraints from the vehicle odometry and the registration of consecutive and non-consecutive frames. The graph is incrementally optimized using the g2o framework and the optimized poses are then used to build the mosaic online. We apply the method on data gathered on real conditions and show that the resulting sonar mosaic closely matches both the offline generated mosaic as well as ground truth measurements while operating under real-time constraints.

SLAM with SC-PHD Filters: An Underwater Vehicle Application

The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position.

A robust visual servo control scheme with prescribed performance for an autonomous underwater vehicle

This paper describes the design and implementation of a visual servo control scheme for an Autonomous Underwater Vehicle (AUV). The purpose of the control scheme is to navigate and stabilize the vehicle towards a visual target. The controller does not utilize the vehicles dynamic model parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances representing ocean currents and waves. The proposed control scheme is of low complexity and can be easily integrated to an embedded control platform of an Autonomous Underwater Vehicle (AUV) with limited power and computational resources. Moreover, through the appropriate selection of certain performance functions, the proposed scheme guarantees that the target lies inside the onboard cameras field of view for all time. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona 500 AUV.

Uncertainty-driven survey path planning for bathymetric mapping

We present a novel survey path planning technique which minimizes the robots position uncertainty along the planned path while taking into account area coverage performance. The proposed technique especially targets bathymetric mapping applications and respects application constraints such as the desire to survey in parallel tracks and to avoid turns in the target area to maximize sonar measurements quality. While accounting for uncertainty in the survey planning process can lead to more accurate data products, existing survey planning tools typically ignore it. Our method bridges this gap using the saliency on an a priori map to predict how the terrain will affect the robots belief at every point on the target area. Based on this magnitude, we provide an algorithm that computes the order in which to trace parallel tracks to cover the target area minimizing the overall uncertainty along the path. A particle filter keeps track of the robots position uncertainty during the planning process and, in order to find useful loop-closures for mapping, crossing tracks that visit salient locations are added when the uncertainty surpasses a user-provided threshold. We test our method on real-world datasets collected off the coasts of Spain, Greece and Australia. We evaluate the expected robots position uncertainty along the planned paths and assess their associated mapping performance using a bathymetric mapping algorithm. Results show that our method offers benefits over a standard lawnmower-type path both in terms of position uncertainty and map quality.

Motion control for autonomous underwater vehicles: A robust model — Free approach

This paper describes the design and implementation of a robust position tracking control scheme for an Autonomous Underwater Vehicle (AUV). The proposed controller does not require knowledge of the vehicles dynamic parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances acting on the vehicle. The resulting scheme is of low complexity and computational cost and thus can be easily integrated to an embedded control platform of an AUV. The proposed control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona500 AUV into two different missions: a) navigation and stabilization to a specific configuration, b) meandrus-like trajectory tracking. In both cases the vehicle was under the influence of time-varying external disturbances caused by a high-pressure water jet installed on the Girona500 manipulator.