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High contact density environments are becoming ubiquitous in autonomous marine vehicle (AMV) operations. Safely managing these environments and their mission greatly taxes platforms. AMV collisions will likely increase as contact density increases. In situations where AMVs are not performing a collaborative mission but are using shared physical space such as multiple vehicles in the same harbor, a high demand exists for safe and efficient operation to minimize mission track deviations while preserving the safety and integrity of mission platforms. With no existing protocol for collision avoidance of AMVs, much effort to date has focused on individual ad hoc collision avoidance approaches that are self-serving, lack the uniformity of fleet-distributed protocols, and disregard the overall fleet efficiency when scaled to being in a contact-dense environment. This research shows that by applying interval programming and a collision avoidance protocol such as the International Regulations for Prevention of Collisions at Sea (COLREGS) to a fleet of AMVs operating in the same geographic area, the fleet achieves nearly identical efficiency concurrent with significant reductions in the collisions observed. A basic collision avoidance protocol was analyzed against a COLREGS-based algorithm while parameters key to collision avoidance were studied using Monte Carlo methods and regression analysis of both real-world and simulated statistical data. A testing metric was proposed for declaring AMVs as "COLREGS-compliant" for at-sea operations. This work tested five AMVs simultaneously with COLREGS collision avoidance-the largest test known to date.

CIVINS (Civilian Institutions) Thesis document, Autonomous platforms and systems are becoming ever more prevalent. They have become smaller, cheaper, have longer duration times, and now more than ever, more capable of processing large amounts of information. Despite these significant technological advances, there is still a level of distrust for the public autonomous systems. In marine and underwater vehicles, autonomy is particularly important being that communications to and from those vehicles are limited, either due to the length of the mission, the distance from their human operators, the sheer number of vehicles being used, or the data transfer rate available from a remote operator to an underwater vehicle through acoustics. The premise for this research is to use the MOOS-IvP code architecture, developed at MIT, to promote and advance marine vehicle autonomy collective knowledge through a project called Hunter-Prey. In this scenario, two or more surface vehicles attempt to cooperatively track an evading underwater target using range-only sensors, and ultimately maneuver into position for a "kill" using a simulated depth charge. This scenario will be distributed to the public though academic institutions and interested parties, who will submit code for the vehicles to compete against one another. The goal for this project is to create and foster an open-source environment where parties can compete and cooperate toward a common goal, the advancement of marine vehicle autonomy. In this paper, the Hunter-Prey scenario is developed, a nominal solution is created, and the parameters for the scenario are analyzed using regression testing through simulation and statistical analysis., http://archive.org/details/cooperativeutono1094540210

This paper describes a system for reacquiring features of interest in a shallow-water ocean environment, using autonomous underwater vehicles (AUVs) equipped with low-cost sonar and navigation sensors. In performing mine countermeasures, it is critical to enable AUVs to navigate accurately to previously mapped objects of interest in the water column or on the seabed, for further assessment or remediation. An important aspect of the overall system design is to keep the size and cost of the reacquisition vehicle as low as possible, as it may potentially be destroyed in the reacquisition mission. This low-cost requirement prevents the use of sophisticated AUV navigation sensors, such as a Doppler velocity log (DVL) or an inertial navigation system (INS). Our system instead uses the Proviewer 900-kHz imaging sonar from Blueview Technologies, which produces forward-looking sonar (FLS) images at ranges up to 40 m at approximately 4 Hz. In large volumes, it is hoped that this sensor can be manufactured at low cost. Our approach uses a novel simultaneous localization and mapping (SLAM) algorithm that detects and tracks features in the FLS images to renavigate to a previously mapped target. This feature-based navigation (FBN) system incorporates a number of recent advances in pose graph optimization algorithms for SLAM. The system has undergone extensive field testing over a period of more than four years, demonstrating the potential for the use of this new approach for feature reacquisition. In this report, we review the methodologies and components of the FBN system, describe the system's technological features, review the performance of the system in a series of extensive in-water field tests, and highlight issues for future research., QC 20130822

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Sage for personal use, not for redistribution. The definitive version was published in International Journal of Robotics Research 31 (2012): 216-235, doi:10.1177/0278364911430419., We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency., M. Kaess, H. Johannsson and J. Leonard were partially supported by ONR grants N00014-06-1-0043 and N00014-10-1-0936. F. Dellaert and R. Roberts were partially supported by NSF, award number 0713162, “RI: Inference in Large-Scale Graphical Models”. V. Ila has been partially supported by the Spanish MICINN under the Programa Nacional de Movilidad de Recursos Humanos de Investigación.

In this paper we present a system for capturing large scale dense maps in an online setting with a low cost RGB-D sensor. Central to this work is the use of an “as-rigid-aspossible” space deformation for efficient dense map correction in a pose graph optimisation framework. By combining pose graph optimisation with non-rigid deformation of a dense map we are able to obtain highly accurate dense maps over large scale trajectories that are both locally and globally consistent. With low latency in mind we derive an incremental method for deformation graph construction, allowing multi-million point maps to be captured over hundreds of metres in real-time. We provide benchmark results on a well established RGBD SLAM dataset demonstrating the accuracy of the system and also provide a number of our own datasets which cover a wide range of environments, both indoors, outdoors and across multiple floors.

Autonomous platforms and systems are becoming ever more prevalent. They have become smaller, cheaper, have longer duration times, and now more than ever, more capable of processing large amounts of information. Despite these significant technological advances, there is still a level of distrust for the public autonomous systems. In marine and underwater vehicles, autonomy is particularly important being that communications to and from those vehicles are limited, either due to the length of the mission, the distance from their human operators, the sheer number of vehicles being used, or the data transfer rate available from a remote operator to an underwater vehicle through acoustics. The premise for this research is to use the MOOS-IvP code architecture, developed at MIT, to promote and advance marine vehicle autonomy collective knowledge through a project called Hunter-Prey. In this scenario, two or more surface vehicles attempt to cooperatively track an evading underwater target using range-only sensors, and ultimately maneuver into position for a "kill" using a simulated depth charge. This scenario will be distributed to the public though academic institutions and interested parties, who will submit code for the vehicles to compete against one another. The goal for this project is to create and foster an open-source environment where parties can compete and cooperate toward a common goal, the advancement of marine vehicle autonomy. In this paper, the Hunter-Prey scenario is developed, a nominal solution is created, and the parameters for the scenario are analyzed using regression testing through simulation and statistical analysis.

This paper describes a system for performing real-time multi-session visual mapping in large-scale environments. Multi-session mapping considers the problem of combining the results of multiple simultaneous localisation and mapping (SLAM) missions performed repeatedly over time in the same environment. The goal is to robustly combine multiple maps in a common metrical coordinate system, with consistent estimates of uncertainty. Our work employs incremental smoothing and mapping (iSAM) as the underlying SLAM state estimator and uses an improved appearance-based method for detecting loop closures within single mapping sessions and across multiple sessions. To stitch together pose graph maps from multiple visual mapping sessions, we employ spatial separator variables, called anchor nodes, to link together multiple relative pose graphs. The system architecture consists of a separate front-end for computing visual odometry and windowed bundle adjustment on individual sessions, in conjunction with a back-end for performing the place recognition and multi-session mapping. We provide experimental results for real-time multisession visual mapping on wheeled and handheld datasets in the MIT Stata Center. These results demonstrate key capabilities that will serve as a foundation for future work in large-scale persistent visual mapping.

Accurate navigation is a fundamental requirement for robotic systems—marine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely low drift rates, the localization accuracy decays monotonically with distance traveled. Other approaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well as its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes relative to a prior map so as to revisit a specific feature, while the third builds an accurate model of an underwater structure which is consistent and complete. In particular we demonstrate that each approach can be abstracted to a core problem of incremental estimation within a sparse graph of the AUV’s trajectory and the locations of features of interest which can be updated and optimized in real time on board the AUV., QC 20220308

In this paper, we demonstrate a system for temporally scalable visual SLAM using a reduced pose graph representation. Unlike previous visual SLAM approaches that use key frames, our approach continually uses new measurements to improve the map, yet achieves efficiency by avoiding adding redundant frames and not using marginalization to reduce the graph. To evaluate our approach, we present results using an online binocular visual SLAM system that uses place recognition for both robustness and multi-session operation. To allow large scale indoor mapping, our system automatically handles elevator rides based on accelerometer data. We demonstrate long-term mapping in a large multi-floor building, using approximately nine hours of data collected over the course of six months. Our results illustrate the capability of our visual SLAM system to scale in size with the area of exploration instead of the time of exploration., Contract no. N00014-12-1-0020

In this paper we present an extension to the KinectFusion algorithm that permits dense mesh-based mapping of extended scale environments in real-time. This is achieved through (i) altering the original algorithm such that the region of space being mapped by the KinectFusion algorithm can vary dynamically, (ii) extracting a dense point cloud from the regions that leave the KinectFusion volume due to this variation, and, (iii) incrementally adding the resulting points to a triangular mesh representation of the environment. The system is implemented as a set of hierarchical multi-threaded components which are capable of operating in real-time. The architecture facilitates the creation and integration of new modules with minimal impact on the performance on the dense volume tracking and surface reconstruction modules. We provide experimental results demonstrating the system’s ability to map areas considerably beyond the scale of the original KinectFusion algorithm including a two story apartment and an extended sequence taken from a car at night. In order to overcome failure of the iterative closest point (ICP) based odometry in areas of low geometric features we have evaluated the Fast Odometry from Vision (FOVIS) system as an alternative. We provide a comparison between the two approaches where we show a trade off between the reduced drift of the visual odometry approach and the higher local mesh quality of the ICP-based approach. Finally we present ongoing work on incorporating full simultaneous localisation and mapping (SLAM) pose-graph optimisation.

This paper describes extensions to the Kintinuous algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused real-time surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features.

In this paper, we demonstrate a system for temporally scalable visual SLAM using a reduced pose graph representation. Unlike previous visual SLAM approaches that use keyframes, our approach continually uses new measurements to improve the map, yet achieves efficiency by avoiding adding redundant frames and not using marginalization to reduce the graph. To evaluate our approach, we present results using an online binocular visual SLAM system that uses place recognition for both robustness and multi-session operation. To allow large-scale indoor mapping, our system automatically handles elevator rides based on accelerometer data. We demonstrate long-term mapping in a large multi-floor building, using approximately nine hours of data collected over the course of six months. Our results illustrate the capability of our visual SLAM system to scale in size with the area of exploration instead of the time of exploration.

This paper describes extensions to the Kintinuous [1] algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused realtime surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features.

In this paper we show how to carry out robust place recognition using both near and far information provided by a stereo camera. Visual appearance is known to be very useful in place recognition tasks. In recent years, it has been shown that taking geometric information also into account further improves system robustness. Stereo visual systems provide 3D information and texture of nearby regions, as well as an image of far regions. In order to make use of all this information, our system builds two probabilistic undirected graphs, each considering either near or far information. Inference is carried out in the framework of conditional random fields. We evaluate our algorithm in public indoor and outdoor datasets from the RAWSEEDS project and in an outdoor dataset obtained at the MIT campus. Results show that this combination of information is very useful to solve challenging cases of perceptual aliasing.

This paper presents an on-line nonlinear least squares algorithm for multi-sensor autonomous underwater vehicle (AUV) navigation. The approach integrates the global constraints of range to and GPS position of a surface vehicle or buoy communicated via acoustic modems and relative pose constraints arising from targets detected in side-scan sonar images. The approach utilizes an efficient optimization algorithm, iSAM, which allows for consistent on-line estimation of the entire set of trajectory constraints. The optimized trajectory can then be used to more accurately navigate the AUV, to extend mission duration, and to avoid GPS surfacing. As iSAM provides efficient access to the marginal covariances of previously observed features, automatic data association is greatly simplified — particularly in sparse marine environments. A key feature of our approach is its intended scalability to single surface sensor (a vehicle or buoy) broadcasting its GPS position and simultaneous one-way travel time range (OWTT) to multiple AUVs. We discuss why our approach is scalable as well as robust to modem transmission failure. Results are provided for an ocean experiment using a Hydroid REMUS 100 AUV co-operating with one of two craft: an autonomous surface vehicle (ASV) and a manned support vessel. During these experiments the ranging portion of the algorithm ran online on-board the AUV. Extension of the paradigm to multiple missions via the optimization of successive survey missions (and the resultant sonar mosaics) is also demonstrated., United States. Office of Naval Research (Grant N000140711102)

This document describes the IvP Helm - an Open Source behavior-based autonomy application for unmanned vehicles. IvP is short for interval programming - a technique for representing and solving multi-objective optimizations problems. Behaviors in the IvP Helm are reconciled using multi-objective optimization when in competition with each other for influence of the vehicle. The IvP Helm is written as a MOOS application where MOOS is a set of Open Source publish-subscribe autonomy middleware tools. This document describes the configuration and use of the IvP Helm, provides examples of simple missions and information on how to download and build the software from the MOOS-IvP server at www.moos-ivp.org.

This document describes the MOOS-IvP autonomy software for unmanned marine vehicles and its use in large-scale ocean sensing systems. MOOS-IvP is composed of two open-source software projects funded by the Office of Naval Research. MOOS provides a core autonomy middleware capability, and the MOOS project additionally provides a set of ubiquitous infrastructure utilities. The IvP Helm is the primary component of an additional set of capabilities implemented to form a full marine autonomy suite known as MOOS-IvP. This software and architecture are platform and mission agnostic and allow for a scalable nesting of unmanned vehicle nodes to form large-scale, long-endurance ocean sensing systems composed of heterogeneous platform types with varying degrees of communications connectivity, bandwidth, and latency., Published in Journal of Field Robotics, v27 n6 p834-875, 2010. Sponsored in part by ONR Contract no. N00014-08-1-0011.

This document describes the IvP Helm -- an Open Source behavior-based autonomy application for unmanned vehicles. IvP is short for interval programming -- a technique for representing and solving multi-objective optimizations problems. Behaviors in the IvP Helm are reconciled using multi-objective optimization when in competition with each other for influence of the vehicle. The IvP Helm is written as a MOOS application where MOOS is a set of Open Source publish-subscribe autonomy middleware tools. This document describes the configuration and use of the IvP Helm, provides examples of simple missions and information on how to download and build the software from the MOOS-IvP server at www.moosivp.org.

This paper provides an overview of the MOOS-IvP autonomy software modules. The MOOS-IvP collection of software, i.e., codebase, described here has been developed and is currently maintained by three organizations - Oxford University, Massachusetts Institute of Technology (MIT), and the Naval Undersea Warfare Center (NUWC) Division Newport Rhode Island. The objective of this paper is to provide a comprehensive list of modules and provide for each (a) a general description of functionality, (b) dependency relationships to other modules, (c) rough order of magnitude in complexity or size, (d) authorship, and (e) current and planned distribution access.

This document describes how to extend the suite of MOOS applications and IvP Helm behaviors distributed with the MOOS-IvP software bundle from www.moos-ivp.org. It covers (a) a straw-man repository with a place-holder MOOS application and IvP Behavior, with a working CMake build structure, (b) a brief overview of the MOOS application class with an example application, (c) an overview of the IvP Behavior class with an example behavior, and (d) the IvPBuild Toolbox for generation of objective functions within behaviors.

This document describes the IvP Helm - an Open Source behavior-based autonomy application for unmanned vehicles. IvP is short for interval programming - a technique for representing and solving multi-objective optimizations problems. Behaviors in the IvP Helm are reconciled using multi-objective optimization when in competition with each other for influence of the vehicle. The IvP Helm is written as a MOOS application where MOOS is a set of Open Source publish-subscribe autonomy middleware tools. This document describes the configuration and use of the IvP Helm, provides examples of simple missions and information on how to download and build the software from the MOOS-IvP server at www.moosivp.org.

To develop net-centric, autonomous underwater vehicle sensing concepts for littoral MCM and ASW, exploiting collaborative and environmentally adaptive, bi- and multi-static, passive and active sonar configurations for concurrent detection, classification and localization of subsea and bottom objects., The original document contains color images.

To develop net-centric, autonomous underwater vehicle sensing concepts for littoral MCM and ASW, exploiting collaborative and environmentally adaptive, bi- and multi-static, passive and active sonar configurations for concurrent detection, classification and localization of proud and buried targets., The original document contains color images.

Many current methods of search using autonomous marine vehicles do not adapt to changes in mission objectives or the environment. A cellular-decomposition-based framework for cooperative, adaptive search is proposed that allows multiple search platforms to adapt to changes in both mission objectives and environmental parameters. Software modules for the autonomy framework MOOS-IvP are described that implement this framework. Simulated and experimental results show that it is feasible to combine both pre-planned and adaptive behaviors to effectively search a target area., See also ADM002176. Presented at the MTS/IEEE Oceans 2008 Conference and Exhibition held in Quebec City, Canada on 15-18 September 2008. U.S. Government or Federal Rights License.. The original document contains color images.