1,313 research outputs found
Unifying terrain awareness for the visually impaired through real-time semantic segmentation.
Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework
The role of the research simulator in the systems development of rotorcraft
The potential application of the research simulator to future rotorcraft systems design, development, product improvement evaluations, and safety analysis is examined. Current simulation capabilities for fixed-wing aircraft are reviewed and the requirements of a rotorcraft simulator are defined. The visual system components, vertical motion simulator, cab, and computation system for a research simulator under development are described
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Safe navigation and human-robot interaction in assistant robotic applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
MV-Map: Offboard HD-Map Generation with Multi-view Consistency
While bird's-eye-view (BEV) perception models can be useful for building
high-definition maps (HD-Maps) with less human labor, their results are often
unreliable and demonstrate noticeable inconsistencies in the predicted HD-Maps
from different viewpoints. This is because BEV perception is typically set up
in an 'onboard' manner, which restricts the computation and consequently
prevents algorithms from reasoning multiple views simultaneously. This paper
overcomes these limitations and advocates a more practical 'offboard' HD-Map
generation setup that removes the computation constraints, based on the fact
that HD-Maps are commonly reusable infrastructures built offline in data
centers. To this end, we propose a novel offboard pipeline called MV-Map that
capitalizes multi-view consistency and can handle an arbitrary number of frames
with the key design of a 'region-centric' framework. In MV-Map, the target
HD-Maps are created by aggregating all the frames of onboard predictions,
weighted by the confidence scores assigned by an 'uncertainty network'. To
further enhance multi-view consistency, we augment the uncertainty network with
the global 3D structure optimized by a voxelized neural radiance field
(Voxel-NeRF). Extensive experiments on nuScenes show that our MV-Map
significantly improves the quality of HD-Maps, further highlighting the
importance of offboard methods for HD-Map generation.Comment: ICCV 202
Computing von Kries Illuminant Changes by Piecewise Inversion of Cumulative Color Histograms
We present a linear algorithm for the computation of the illuminant change occurring between two color pictures of a scene. We model the light variations with the von Kries diagonal transform and we estimate it by minimizing a dissimilarity measure between the piecewise inversions of the cumulative color histograms of the considered images. We also propose a method for illuminant invariant image recognition based on our von Kries transform estimate
Toward Robots with Peripersonal Space Representation for Adaptive Behaviors
The abilities to adapt and act autonomously in an unstructured and
human-oriented environment are necessarily vital for the next generation of
robots, which aim to safely cooperate with humans. While this adaptability
is natural and feasible for humans, it is still very complex and challenging
for robots. Observations and findings from psychology and neuroscience in
respect to the development of the human sensorimotor system can inform
the development of novel approaches to adaptive robotics.
Among these is the formation of the representation of space closely surrounding
the body, the Peripersonal Space (PPS) , from multisensory sources
like vision, hearing, touch and proprioception, which helps to facilitate human
activities within their surroundings.
Taking inspiration from the virtual safety margin formed by the PPS representation
in humans, this thesis first constructs an equivalent model of the
safety zone for each body part of the iCub humanoid robot. This PPS layer
serves as a distributed collision predictor, which translates visually detected
objects approaching a robot\u2019s body parts (e.g., arm, hand) into the probabilities
of a collision between those objects and body parts. This leads to
adaptive avoidance behaviors in the robot via an optimization-based reactive
controller. Notably, this visual reactive control pipeline can also seamlessly
incorporate tactile input to guarantee safety in both pre- and post-collision
phases in physical Human-Robot Interaction (pHRI). Concurrently, the controller
is also able to take into account multiple targets (of manipulation reaching tasks) generated by a multiple Cartesian point planner. All components,
namely the PPS, the multi-target motion planner (for manipulation
reaching tasks), the reaching-with-avoidance controller and the humancentred
visual perception, are combined harmoniously to form a hybrid control
framework designed to provide safety for robots\u2019 interactions in a cluttered
environment shared with human partners.
Later, motivated by the development of manipulation skills in infants, in
which the multisensory integration is thought to play an important role, a
learning framework is proposed to allow a robot to learn the processes of
forming sensory representations, namely visuomotor and visuotactile, from
their own motor activities in the environment. Both multisensory integration
models are constructed with Deep Neural Networks (DNNs) in such a
way that their outputs are represented in motor space to facilitate the robot\u2019s
subsequent actions
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
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