2,775 research outputs found
Appearance Modelling and Reconstruction for Navigation in Minimally Invasive Surgery
Minimally invasive surgery is playing an increasingly important role for patient
care. Whilst its direct patient benefit in terms of reduced trauma,
improved recovery and shortened hospitalisation has been well established,
there is a sustained need for improved training of the existing procedures
and the development of new smart instruments to tackle the issue of visualisation,
ergonomic control, haptic and tactile feedback. For endoscopic
intervention, the small field of view in the presence of a complex anatomy
can easily introduce disorientation to the operator as the tortuous access
pathway is not always easy to predict and control with standard endoscopes.
Effective training through simulation devices, based on either virtual reality
or mixed-reality simulators, can help to improve the spatial awareness,
consistency and safety of these procedures.
This thesis examines the use of endoscopic videos for both simulation
and navigation purposes. More specifically, it addresses the challenging
problem of how to build high-fidelity subject-specific simulation environments
for improved training and skills assessment. Issues related to mesh
parameterisation and texture blending are investigated. With the maturity
of computer vision in terms of both 3D shape reconstruction and localisation
and mapping, vision-based techniques have enjoyed significant interest
in recent years for surgical navigation. The thesis also tackles the problem
of how to use vision-based techniques for providing a detailed 3D map and
dynamically expanded field of view to improve spatial awareness and avoid
operator disorientation. The key advantage of this approach is that it does
not require additional hardware, and thus introduces minimal interference
to the existing surgical workflow. The derived 3D map can be effectively
integrated with pre-operative data, allowing both global and local 3D navigation
by taking into account tissue structural and appearance changes.
Both simulation and laboratory-based experiments are conducted throughout
this research to assess the practical value of the method proposed
Reconfigurable AUV for Intervention Missions: A Case Study on Underwater Object Recovery
Starting in January 2009, the RAUVI (Reconfigurable Autonomous Underwater Vehicle for Intervention Missions) project is a 3-year coordinated research action funded by the Spanish Ministry of Research and Innovation. In this paper, the state of progress after 2 years of continuous research is reported. As a first experimental validation of the complete system, a search and recovery problem is addressed, consisting of finding and recovering a flight data recorder placed at an unknown position at the bottom of a water tank. An overview of the techniques used to successfully solve the problem in an autonomous way is provided. The obtained results are very promising and are the first step toward the final test in shallow water at the end of 2011
ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments
Generating natural and physically feasible motions for legged robots has been
a challenging problem due to its complex dynamics. In this work, we introduce a
novel learning-based framework of autoregressive motion planner (ARMP) for
quadruped locomotion and navigation. Our method can generate motion plans with
an arbitrary length in an autoregressive fashion, unlike most offline
trajectory optimization algorithms for a fixed trajectory length. To this end,
we first construct the motion library by solving a dense set of trajectory
optimization problems for diverse scenarios and parameter settings. Then we
learn the motion manifold from the dataset in a supervised learning fashion. We
show that the proposed ARMP can generate physically plausible motions for
various tasks and situations. We also showcase that our method can be
successfully integrated with the recent robot navigation frameworks as a
low-level controller and unleash the full capability of legged robots for
complex indoor navigation.Comment: Submitted to IRO
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change
Direct visual localization has recently enjoyed a resurgence in popularity
with the increasing availability of cheap mobile computing power. The
competitive accuracy and robustness of these algorithms compared to
state-of-the-art feature-based methods, as well as their natural ability to
yield dense maps, makes them an appealing choice for a variety of mobile
robotics applications. However, direct methods remain brittle in the face of
appearance change due to their underlying assumption of photometric
consistency, which is commonly violated in practice. In this paper, we propose
to mitigate this problem by training deep convolutional encoder-decoder models
to transform images of a scene such that they correspond to a previously-seen
canonical appearance. We validate our method in multiple environments and
illumination conditions using high-fidelity synthetic RGB-D datasets, and
integrate the trained models into a direct visual localization pipeline,
yielding improvements in visual odometry (VO) accuracy through time-varying
illumination conditions, as well as improved metric relocalization performance
under illumination change, where conventional methods normally fail. We further
provide a preliminary investigation of transfer learning from synthetic to real
environments in a localization context. An open-source implementation of our
method using PyTorch is available at https://github.com/utiasSTARS/cat-net.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane,
Australia, May 21-25, 201
Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection
In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation
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