2,194 research outputs found
Formation and life-time of memory domains in the dissipative quantum model of brain
We show that in the dissipative quantum model of brain the time-dependence of
the frequencies of the electrical dipole wave quanta leads to the dynamical
organization of the memories in space (i.e. to their localization in more or
less diffused regions of the brain) and in time (i.e. to their longer or
shorter life-time). The life-time and the localization in domains of the memory
states also depend on internal parameters and on the number of links that the
brain establishes with the external world. These results agree with the
physiological observations of the dynamic formation of neural circuitry which
grows as brain develops and relates to external world.Comment: LaTex file, 4 figures, 19 page
Toward an object-based semantic memory for long-term operation of mobile service robots
Throughout a lifetime of operation, a mobile service robot needs to acquire, store and update its knowledge of a working environment. This includes the ability to identify and track objects in different places, as well as using this information for interaction with humans. This paper introduces a long-term updating mechanism, inspired by the modal model of human memory, to enable a mobile robot to maintain its knowledge of a changing environment. The memory model is integrated with a hybrid map that represents the global topology and local geometry of the environment, as well as the respective 3D location of objects. We aim to enable the robot to use this knowledge to help humans by suggesting the most likely locations of specific objects in its map. An experiment using omni-directional vision demonstrates the ability to track the movements of several objects in a dynamic environment over an extended period of time
Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression
In this paper, we revisit batch state estimation through the lens of Gaussian
process (GP) regression. We consider continuous-discrete estimation problems
wherein a trajectory is viewed as a one-dimensional GP, with time as the
independent variable. Our continuous-time prior can be defined by any
nonlinear, time-varying stochastic differential equation driven by white noise;
this allows the possibility of smoothing our trajectory estimates using a
variety of vehicle dynamics models (e.g., `constant-velocity'). We show that
this class of prior results in an inverse kernel matrix (i.e., covariance
matrix between all pairs of measurement times) that is exactly sparse
(block-tridiagonal) and that this can be exploited to carry out GP regression
(and interpolation) very efficiently. When the prior is based on a linear,
time-varying stochastic differential equation and the measurement model is also
linear, this GP approach is equivalent to classical, discrete-time smoothing
(at the measurement times); when a nonlinearity is present, we iterate over the
whole trajectory to maximize accuracy. We test the approach experimentally on a
simultaneous trajectory estimation and mapping problem using a mobile robot
dataset.Comment: Submitted to Autonomous Robots on 20 November 2014, manuscript #
AURO-D-14-00185, 16 pages, 7 figure
Cross-view Self-localization from Synthesized Scene-graphs
Cross-view self-localization is a challenging scenario of visual place
recognition in which database images are provided from sparse viewpoints.
Recently, an approach for synthesizing database images from unseen viewpoints
using NeRF (Neural Radiance Fields) technology has emerged with impressive
performance. However, synthesized images provided by these techniques are often
of lower quality than the original images, and furthermore they significantly
increase the storage cost of the database. In this study, we explore a new
hybrid scene model that combines the advantages of view-invariant appearance
features computed from raw images and view-dependent spatial-semantic features
computed from synthesized images. These two types of features are then fused
into scene graphs, and compressively learned and recognized by a graph neural
network. The effectiveness of the proposed method was verified using a novel
cross-view self-localization dataset with many unseen views generated using a
photorealistic Habitat simulator.Comment: 5 pages, 5 figures, technical repor
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
Color-depth cameras (RGB-D cameras) have become the primary sensors in most
robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and
extrinsic calibration that generally does not meet the accuracy requirements
needed by many robotics applications (e.g., highly accurate 3D environment
reconstruction and mapping, high precision object recognition and localization,
...). In this paper, we propose a human-friendly, reliable and accurate
calibration framework that enables to easily estimate both the intrinsic and
extrinsic parameters of a general color-depth sensor couple. Our approach is
based on a novel two components error model. This model unifies the error
sources of RGB-D pairs based on different technologies, such as
structured-light 3D cameras and time-of-flight cameras. Our method provides
some important advantages compared to other state-of-the-art systems: it is
general (i.e., well suited for different types of sensors), based on an easy
and stable calibration protocol, provides a greater calibration accuracy, and
has been implemented within the ROS robotics framework. We report detailed
experimental validations and performance comparisons to support our statements
- …