Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Preliminary Deep Water Results in Single-Beacon One-Way-Travel-Time Acoustic Navigation for Underwater Vehicles

This paper reports the development and experimental evaluation of a novel navigation system for underwater vehicles that employs Doppler sonar, synchronous clocks, and acoustic modems to achieve simultaneous acoustic communication and navigation. The system reported herein, which is employed to renavigate the vehicle in post-processing, forms the basis for a vehicle-based real-time navigation system. Existing high-precision absolute navigation techniques for underwater vehicles are impractical over long length scales and lack scalability for simultaneously navigating multiple vehicles. The navigation method reported in this paper relies on a single moving reference beacon, eliminating the requirement for the underwater vehicle to remain in a bounded navigable area. The use of underwater modems and synchronous clocks enables range measurements based on one-way time-of-flight information from acoustic data packet broadcasts. The acoustic data packets are broadcast from the single, moving reference beacon and can be received simultaneously by multiple vehicles within acoustic range. We report experimental results from the first deep-water evaluation of this method using data collected from an autonomous underwater vehicle (AUV) survey carried out in 4000 m of water on the southern Mid-Atlantic Ridge. We report a comparative experimental evaluation of the navigation fixes provided by the proposed synchronous acoustic navigation system in comparison to navigation fixes obtained by an independent conventional long baseline acoustic navigation system.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86040/1/swebster-7.pd

Optimal path shape for range-only underwater target localization using a Wave Glider

Underwater localization using acoustic signals is one of the main components in a navigation system for an autonomous underwater vehicle (AUV) as a more accurate alternative to dead-reckoning techniques. Although different methods based on the idea of multiple beacons have been studied, other approaches use only one beacon, which reduces the system’s costs and deployment complexity. The inverse approach for single-beacon navigation is to use this method for target localization by an underwater or surface vehicle. In this paper, a method of range-only target localization using a Wave Glider is presented, for which simulations and sea tests have been conducted to determine optimal parameters to minimize acoustic energy use and search time, and to maximize location accuracy and precision. Finally, a field mission is presented, where a Benthic Rover (an autonomous seafloor vehicle) is localized and tracked using minimal human intervention. This mission shows, as an example, the power of using autonomous vehicles in collaboration for oceanographic research.Peer ReviewedPostprint (author's final draft

SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.Comment: 9 pages, 8 figures, extended version of paper submitted to ICRA 202

Localization, Mapping and SLAM in Marine and Underwater Environments

The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots

Efficient AUV Navigation Fusing Acoustic Ranging and Side-scan Sonar

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

Self consistent bathymetric mapping from robotic vehicles in the deep ocean

Submitted In partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution June 2005Obtaining accurate and repeatable navigation for robotic vehicles in the deep ocean is difficult and consequently a limiting factor when constructing vehicle-based bathymetric maps. This thesis presents a methodology to produce self-consistent maps and simultaneously improve vehicle position estimation by exploiting accurate local navigation and utilizing terrain relative measurements. It is common for errors in the vehicle position estimate to far exceed the errors associated with the acoustic range sensor. This disparity creates inconsistency when an area is imaged multiple times and causes artifacts that distort map integrity. Our technique utilizes small terrain "submaps" that can be pairwise registered and used to additionally constrain the vehicle position estimates in accordance with actual bottom topography. A delayed state Kalman filter is used to incorporate these sub-map registrations as relative position measurements between previously visited vehicle locations. The archiving of previous positions in a filter state vector allows for continual adjustment of the sub-map locations. The terrain registration is accomplished using a two dimensional correlation and a six degree of freedom point cloud alignment method tailored for bathymetric data. The complete bathymetric map is then created from the union of all sub-maps that have been aligned in a consistent manner. Experimental results from the fully automated processing of a multibeam survey over the TAG hydrothermal structure at the Mid-Atlantic ridge are presented to validate the proposed method.This work was funded by the CenSSIS ERC of the Nation Science Foundation under grant EEC-9986821 and in part by the Woods Hole Oceanographic Institution through a grant from the Penzance Foundation