688 research outputs found
Exploring Convolutional Networks for End-to-End Visual Servoing
Present image based visual servoing approaches rely on extracting hand
crafted visual features from an image. Choosing the right set of features is
important as it directly affects the performance of any approach. Motivated by
recent breakthroughs in performance of data driven methods on recognition and
localization tasks, we aim to learn visual feature representations suitable for
servoing tasks in unstructured and unknown environments. In this paper, we
present an end-to-end learning based approach for visual servoing in diverse
scenes where the knowledge of camera parameters and scene geometry is not
available a priori. This is achieved by training a convolutional neural network
over color images with synchronised camera poses. Through experiments performed
in simulation and on a quadrotor, we demonstrate the efficacy and robustness of
our approach for a wide range of camera poses in both indoor as well as outdoor
environments.Comment: IEEE ICRA 201
Linear Quasi-Parallax SfM for various classes of biological eyes
Ph.DDOCTOR OF PHILOSOPH
Miniature curved artificial compound eyes.
International audienceIn most animal species, vision is mediated by compound eyes, which offer lower resolution than vertebrate single-lens eyes, but significantly larger fields of view with negligible distortion and spherical aberration, as well as high temporal resolution in a tiny package. Compound eyes are ideally suited for fast panoramic motion perception. Engineering a miniature artificial compound eye is challenging because it requires accurate alignment of photoreceptive and optical components on a curved surface. Here, we describe a unique design method for biomimetic compound eyes featuring a panoramic, undistorted field of view in a very thin package. The design consists of three planar layers of separately produced arrays, namely, a microlens array, a neuromorphic photodetector array, and a flexible printed circuit board that are stacked, cut, and curved to produce a mechanically flexible imager. Following this method, we have prototyped and characterized an artificial compound eye bearing a hemispherical field of view with embedded and programmable low-power signal processing, high temporal resolution, and local adaptation to illumination. The prototyped artificial compound eye possesses several characteristics similar to the eye of the fruit fly Drosophila and other arthropod species. This design method opens up additional vistas for a broad range of applications in which wide field motion detection is at a premium, such as collision-free navigation of terrestrial and aerospace vehicles, and for the experimental testing of insect vision theories
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
In this paper, we develop a modular neural network for real-time semantic
mapping in uncertain environments, which explicitly updates per-voxel
probabilistic distributions within a neural network layer. Our approach
combines the reliability of classical probabilistic algorithms with the
performance and efficiency of modern neural networks. Although robotic
perception is often divided between modern differentiable methods and classical
explicit methods, a union of both is necessary for real-time and trustworthy
performance. We introduce a novel Convolutional Bayesian Kernel Inference
(ConvBKI) layer which incorporates semantic segmentation predictions online
into a 3D map through a depthwise convolution layer by leveraging conjugate
priors. We compare ConvBKI against state-of-the-art deep learning approaches
and probabilistic algorithms for mapping to evaluate reliability and
performance. We also create a Robot Operating System (ROS) package of ConvBKI
and test it on real-world perceptually challenging off-road driving data.Comment: arXiv admin note: text overlap with arXiv:2209.1066
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