20 research outputs found
Real-time Monocular Object SLAM
We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of binary words, which provides live detections with a database
of 500 3D objects. The two components work together and benefit each other: the
SLAM algorithm accumulates information from the observations of the objects,
anchors object features to especial map landmarks and sets constrains on the
optimization. At the same time, objects partially or fully located within the
map are used as a prior to guide the recognition algorithm, achieving higher
recall. We evaluate our proposal on five real environments showing improvements
on the accuracy of the map and efficiency with respect to other
state-of-the-art techniques
LightDepth: Single-View Depth Self-Supervision from Illumination Decline
Single-view depth estimation can be remarkably effective if there is enough
ground-truth depth data for supervised training. However, there are scenarios,
especially in medicine in the case of endoscopies, where such data cannot be
obtained. In such cases, multi-view self-supervision and synthetic-to-real
transfer serve as alternative approaches, however, with a considerable
performance reduction in comparison to supervised case. Instead, we propose a
single-view self-supervised method that achieves a performance similar to the
supervised case. In some medical devices, such as endoscopes, the camera and
light sources are co-located at a small distance from the target surfaces.
Thus, we can exploit that, for any given albedo and surface orientation, pixel
brightness is inversely proportional to the square of the distance to the
surface, providing a strong single-view self-supervisory signal. In our
experiments, our self-supervised models deliver accuracies comparable to those
of fully supervised ones, while being applicable without depth ground-truth
data
An innovative quick solidifying technique for the forensic investigation of brain circulation using addition silicones
Post-mortem study of the brain is extremely relevant to medico-legal autopsies. However, it can be difficult to handle due to its fragility. This article presents a study on the development of an arterial solidifying technique that can be applied to analyze arterial circulation, consequently easing the handling and later diagnosis of diseases in this anatomical site. Vinylpolysiloxane silicone is introduced into the internal carotid arteries until it completely fills the arterial tree, creating a detailed model of the arterial's internal anatomy. This technique is fast, easy to apply and requires no previous tissue fixation. In addition, it allows for further toxicological and pathological tests. In conclusion, this technique represents a simple, sensitive and efficient method to employ in conventional autopsies, which can help in the diagnosis of death
Robust mapping and localization in indoor environments using sonar data
In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison. Key word
Tardós, “Mapping large loops with a single hand-held camera
Abstract — This paper 1 presents a method for Simultaneous Localization and Mapping (SLAM) relying on a monocular camera as the only sensor which is able to build outdoor, closedloop maps much larger than previously achieved with such input. Our system, based on the Hierarchical Map approach [1], builds independent local maps in real-time using the EKF-SLAM technique and the inverse depth representation proposed in [2]. The main novelty in the local mapping process is the use of a data association technique that greatly improves its robustness in dynamic and complex environments. A new visual map matching algorithm stitches these maps together and is able to detect large loops automatically, taking into account the unobservability of scale intrinsic to pure monocular SLAM. The loop closing constraint is applied at the upper level of the Hierarchical Map in near real-time. We present experimental results demonstrating monocular SLAM as a human carries a camera over long walked trajectories in outdoor areas with people and other clutter, even in the more difficult case of forward-looking camera, and show the closing of loops of several hundred meters. I