Simultaneous Parameter Calibration, Localization, and Mapping

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms. (C) 2012 Taylor & Francis and The Robotics Society of Japa

Efficient Bayesian inference for harmonic models via adaptive posterior factorization

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NEUROCOMPUTING, [VOL72, ISSUE 1-3, (2008)] DOI10.1016/j.neucom.2007.12.05

Contributions to automated realtime underwater navigation

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

Model-Based Adaptive Behavior Framework for Optimal Acoustic Communication and Sensing by Marine Robots

In this paper, a hybrid data- and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication. The adaptation framework is implemented within the behavior-based mission-oriented operating suite-interval programming (MOOS-IvP) marine autonomy architecture and uses a new embedded high-fidelity acoustic modeling infrastructure, the generic robotic acoustic model (GRAM), to provide real-time estimates of the acoustic environment under changing environmental and situational scenarios. A set of behaviors that combine adaptation to the current acoustic environment with strategies that extend the decision horizon beyond that of typical behavior-based systems have been developed, implemented, and demonstrated in a series of field experiments and virtual experiments in a MOOS-IvP simulation.United States. Office of Naval Research (Grant N00014-08-1-0011)United States. Office of Naval Research (Grant N00014-08-1-0013)NATO Undersea Research Centre (NURC

Advances in integrating autonomy with acoustic communications for intelligent networks of marine robots

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2013Autonomous marine vehicles are increasingly used in clusters for an array of oceanographic tasks. The effectiveness of this collaboration is often limited by communications: throughput, latency, and ease of reconfiguration. This thesis argues that improved communication on intelligent marine robotic agents can be gained from acting on knowledge gained by improved awareness of the physical acoustic link and higher network layers by the AUV’s decision making software. This thesis presents a modular acoustic networking framework, realized through a C++ library called goby-acomms, to provide collaborating underwater vehicles with an efficient short-range single-hop network. goby-acomms is comprised of four components that provide: 1) losslessly compressed encoding of short messages; 2) a set of message queues that dynamically prioritize messages based both on overall importance and time sensitivity; 3) Time Division Multiple Access (TDMA) Medium Access Control (MAC) with automatic discovery; and 4) an abstract acoustic modem driver. Building on this networking framework, two approaches that use the vehicle’s “intelligence” to improve communications are presented. The first is a “non-disruptive” approach which is a novel technique for using state observers in conjunction with an entropy source encoder to enable highly compressed telemetry of autonomous underwater vehicle (AUV) position vectors. This system was analyzed on experimental data and implemented on a fielded vehicle. Using an adaptive probability distribution in combination with either of two state observer models, greater than 90% compression, relative to a 32-bit integer baseline, was achieved. The second approach is “disruptive,” as it changes the vehicle’s course to effect an improvement in the communications channel. A hybrid data- and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication.I wish to acknowledge the sponsors of this research for their generous support of my tuition, stipend, and research: the WHOI/MIT Joint Program, the MIT Presidential Fellowship, the Office of Naval Research (ONR) # N00014-08-1-0011, # N00014-08-1-0013, and the ONR PlusNet Program Graduate Fellowship, the Defense Advanced Research Projects Agency (DARPA) (Deep Sea Operations: Applied Physical Sciences (APS) Award # APS 11-15 3352-006, APS 11-15-3352-215 ST 2.6 and 2.7

ObjectFlow: A Descriptor for Classifying Traffic Motion

Abstract—We present and evaluate a novel scene descriptor for classifying urban traffic by object motion. Atomic 3D flow vectors are extracted and compensated for the vehicle’s egomo-tion, using stereo video sequences. Votes cast by each flow vector are accumulated in a bird’s eye view histogram grid. Since we are directly using low-level object flow, no prior object detection or tracking is needed. We demonstrate the effectiveness of the proposed descriptor by comparing it to two simpler baselines on the task of classifying more than 100 challenging video sequences into intersection and non-intersection scenarios. Our experiments reveal good classification performance in busy traffic situations, making our method a valuable complement to traditional approaches based on lane markings. I