86 research outputs found
Accurate Gaussian Process Distance Fields with applications to Echolocation and Mapping
This paper introduces a novel method to estimate distance fields from noisy
point clouds using Gaussian Process (GP) regression. Distance fields, or
distance functions, gained popularity for applications like point cloud
registration, odometry, SLAM, path planning, shape reconstruction, etc. A
distance field provides a continuous representation of the scene. It is defined
as the shortest distance from any query point and the closest surface. The key
concept of the proposed method is a reverting function used to turn a
GP-inferred occupancy field into an accurate distance field. The reverting
function is specific to the chosen GP kernel. This paper provides the
theoretical derivation of the proposed method and its relationship to existing
techniques. The improved accuracy compared with existing distance fields is
demonstrated with simulated experiments. The level of accuracy of the proposed
approach enables novel applications that rely on precise distance estimation.
This work presents echolocation and mapping frameworks for ultrasonic-guided
wave sensing in metallic structures. These methods leverage the proposed
distance field with a physics-based measurement model accounting for the
propagation of the ultrasonic waves in the material. Real-world experiments are
conducted to demonstrate the soundness of these frameworks
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
3D reconstruction and motion estimation using forward looking sonar
Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains
including archaeology, oil and gas industry, coral reef monitoring, harbour’s security,
and mine countermeasure missions. As electromagnetic signals do not penetrate
underwater environment, GPS signals cannot be used for AUV navigation, and optical
cameras have very short range underwater which limits their use in most underwater
environments.
Motion estimation for AUVs is a critical requirement for successful vehicle recovery
and meaningful data collection. Classical inertial sensors, usually used for AUV motion
estimation, suffer from large drift error. On the other hand, accurate inertial sensors are
very expensive which limits their deployment to costly AUVs. Furthermore, acoustic
positioning systems (APS) used for AUV navigation require costly installation and
calibration. Moreover, they have poor performance in terms of the inferred resolution.
Underwater 3D imaging is another challenge in AUV industry as 3D information is
increasingly demanded to accomplish different AUV missions. Different systems have
been proposed for underwater 3D imaging, such as planar-array sonar and T-configured
3D sonar. While the former features good resolution in general, it is very expensive and
requires huge computational power, the later is cheaper implementation but requires
long time for full 3D scan even in short ranges.
In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by
proposing relatively affordable methodologies and study different parameters affecting
their performance. We introduce a new motion estimation framework for AUVs which
relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an
Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on
forward looking sonars; the proposed system features cheaper implementation than
planar array sonars and solves the delay problem in T configured 3D sonars
Toward autonomous harbor surveillance
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Includes bibliographical references (p. 105-113).In this thesis we address the problem of drift-free navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach uses only onboard sensors in a simultaneous localization and mapping setting and removes the need for any external infrastructure like acoustic beacons. We extract dense features from a forward-looking imaging sonar and apply pair-wise registration between sonar frames. The registrations are combined with onboard velocity, attitude and acceleration sensors to obtain an improved estimate of the vehicle trajectory. In addition, an architecture for a persistent mapping is proposed. With the intention of handling long term operations and repetitive surveillance tasks. The proposed architecture is flexible and supports different types of vehicles and mapping methods. The design of the system is demonstrated with an implementation of some of the key features of the system. In addition, methods for re-localization are considered. Finally, results from several experiments that demonstrate drift-free navigation in various underwater environments are presented.by Hordur Johannsson.S.M
Acoustic classification of buried objects with mobile sonar platforms
Thesis (Ph. D. in Ocean Engineering)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (p. 229-237).In this thesis, the use of highly mobile sonar platforms is investigated for the purpose of acoustically classifying compact objects on or below the seabed. The extension of existing strategies, including synthetic aperture sonar and conventional imaging, are explored within the context of the buried object problem. In particular, the need to employ low frequencies for seabed penetration is shown to have a significant impact both due to the relative length of the characteristic scattering mechanisms and due to the interface effects on the target scattering. New sonar strategies are also shown that exploit incoherent wide apertures that are created by multiple sonar platforms. For example, target shape can be inverted by mapping the scattered field from the target with a team of receiver vehicles. A single sonar-adaptive sonar platform is shown to have the ability to perform hunting and classification tasks more efficiently than its pre-programmed counterpart. While the monostatic sonar platform is often dominated by the source component, the bistatic or passive receiver platform behavior is controlled by the target response. The sonar-adaptive platform trajectory, however, can result in the platform finishing its classification effort out of position to complete further tasks.(cont.) Within the context of a larger mission, the use of predetermined adaptive behaviors is shown to provide improved detection and classification performance while minimizing the risk to the overall mission. Finally, it is shown that multiple sonar-adaptive platforms can be used to create new sonar strategies for hunting and classifying objects by shape and content. The ability to sample the scattered field from the target across a wide variety of positions allows an analysis of the aspect-dependent behavior of the target. The aspect-dependence of the specular returns indicate the shape of the target, while the secondary returns from an elastic target are also strongly aspect-dependent. These features are exploited for improved classification performance in the buried object hunting mission.by Joseph R. Edwards.Ph.D.in Ocean Engineerin
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