Dynamic positioning of beacon vehicles for cooperative underwater navigation

Autonomous Underwater Vehicles (AUVs) are used for an ever increasing range of applications due to the maturing of the technology. Due to the absence of the GPS signal underwater, the correct estimation of its position is a challenge for submerged vehicles. One promising strategy to mitigate this problem is to use a group of AUVs where one or more assume the role of a beacon vehicle which has a very accurate position estimate due to an expensive navigation suite or frequent surfacings. These beacon vehicles broadcast their position and the remaining survey vehicles can use this position information and intra-vehicle ranges to update their position estimate. The effectiveness of this approach strongly depends on the geometry between the beacon vehicles and the survey vehicles. The trajectories of the beacon vehicles should thus be planned with the goal to minimize the position uncertainty of the survey vehicles. We propose a distributed algorithm which dynamically computes the locally optimal position for a beacon vehicle using only information obtained from broadcast communication of the survey vehicles. It does not need prior information about the survey vehicles' trajectory and can be used for any group size of beacon and survey vehicles.United States. Office of Naval Research (Grant N00014-97-1-0202)United States. Office of Naval Research (Grant N00014-05-1-0255)United States. Office of Naval Research (Grant N00014-02-C- 0210)United States. Office of Naval Research (Grant N00014-07-1-1102

RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation

Many point estimation problems in robotics, computer vision, and machine learning can be formulated as instances of the general problem of minimizing a sparse nonlinear sum-of-squares objective function. For inference problems of this type, each input datum gives rise to a summand in the objective function, and therefore performing online inference corresponds to solving a sequence of sparse nonlinear least-squares minimization problems in which additional summands are added to the objective function over time. In this paper, we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg numerical optimization method suitable for use in online sequential sparse least-squares minimization. As a trust-region method, RISE is naturally robust to objective function nonlinearity and numerical ill-conditioning and is provably globally convergent for a broad class of inferential cost functions (twice-continuously differentiable functions with bounded sublevel sets). Consequently, RISE maintains the speed of current state-of-the-art online sparse least-squares methods while providing superior reliability.United States. Office of Naval Research (Grant N00014-12-1-0093)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-06-1-0043)United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Air Force Research Laboratory (Contract FA8650-11-C-7137

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing

Shape and Pose Recovery from Planar Pushing

Tactile exploration refers to the use of physical interaction to infer object properties. In this work, we study the feasibility of recovering the shape and pose of a movable object from observing a series of contacts. In particular, we approach the problem of estimating the shape and trajectory of a planar object lying on a frictional surface, and being pushed by a frictional probe. The probe, when in contact with the object, makes observations of the location of contact and the contact normal. Our approach draws inspiration from the SLAM problem, where noisy observations of the location of landmarks are used to reconstruct and locate a static environment. In tactile exploration, analogously, we can think of the object as a rigid but moving environment, and of the pusher as a sensor that reports contact points on the boundary of the object. A key challenge to tactile exploration is that, unlike visual feedback, sensing by touch is intrusive in nature. The object moves by the action of sensing. In the 2D version of the problem that we study in this paper, the well understood mechanics of planar frictional pushing provides a motion model that plays the role of odometry. The conjecture we investigate in this paper is whether the models of frictional pushing are sufficiently descriptive to simultaneously estimate the shape and pose of an object from the cumulative effect of a sequence of pushes.National Science Foundation (U.S.) (Award IIS-1427050

Place recognition using near and far visual information

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

Mapping Complex Marine Environments with Autonomous Surface Craft

This paper presents a novel marine mapping system using an Autonomous Surface Craft (ASC). The platform includes an extensive sensor suite for mapping environments both above and below the water surface. A relatively small hull size and shallow draft permits operation in cluttered and shallow environments. We address the Simultaneous Mapping and Localization (SLAM) problem for concurrent mapping above and below the water in large scale marine environments. Our key algorithmic contributions include: (1) methods to account for degradation of GPS in close proximity to bridges or foliage canopies and (2) scalable systems for management of large volumes of sensor data to allow for consistent online mapping under limited physical memory. Experimental results are presented to demonstrate the approach for mapping selected structures along the Charles River in Boston.United States. Office of Naval Research (N00014-06-10043)United States. Office of Naval Research (N00014-05-10244)United States. Office of Naval Research (N00014-07-11102)Massachusetts Institute of Technology. Sea Grant College Program (grant 2007-R/RCM-20

Efficient incremental map segmentation in dense RGB-D maps

In this paper we present a method for incrementally segmenting large RGB-D maps as they are being created. Recent advances in dense RGB-D mapping have led to maps of increasing size and density. Segmentation of these raw maps is a first step for higher-level tasks such as object detection. Current popular methods of segmentation scale linearly with the size of the map and generally include all points. Our method takes a previously segmented map and segments new data added to that map incrementally online. Segments in the existing map are re-segmented with the new data based on an iterative voting method. Our segmentation method works in maps with loops to combine partial segmentations from each traversal into a complete segmentation model. We verify our algorithm on multiple real-world datasets spanning many meters and millions of points in real-time. We compare our method against a popular batch segmentation method for accuracy and timing complexity.United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-12-10020)National Science Foundation (U.S.) (Grant IIS-1318392)Science Foundation Ireland (Strategic Research Cluster Grant 07/SRC/I1168

An Overview of MOOS-IvP and a Users Guide to the IvP Helm - Release 4.2.1

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.United States. Office of Naval Research (Code 311

Analytically-selected multi-hypothesis incremental MAP estimation

In this paper, we introduce an efficient maximum a posteriori (MAP) estimation algorithm, which effectively tracks multiple most probable hypotheses. In particular, due to multimodal distributions arising in most nonlinear problems, we employ a bank of MAP to track these modes (hypotheses). The key idea is that we analytically determine all the posterior modes for the current state at each time step, which are used to generate highly probable hypotheses for the entire trajectory. Moreover, since it is expensive to solve the MAP problem sequentially over time by an iterative method such as Gauss-Newton, in order to speed up its solution, we reuse the previous computations and incrementally update the square-root informationmatrix at every time step, while batch relinearization is performed only periodically or as needed.United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-12-10020)National Science Foundation (U.S.) (IIS-0643680