1,619 research outputs found
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing
localization and segmentation, which are two of the most important vision-based
tasks for robotics. While the goals and techniques used for them were
considered to be different previously, we show that by making use of the
intermediate results of the two modules, their performance can be enhanced at
the same time. Our framework is able to handle both the instantaneous motion
and long-term changes of instances in localization with the help of the
segmentation result, which also benefits from the refined 3D pose information.
We conduct experiments on various datasets, and prove that our framework works
effectively on improving the precision and robustness of the two tasks and
outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo
video can be found at https://youtu.be/Bkt53dAehj
A comprehensive survey on recent deep learning-based methods applied to surgical data
Minimally invasive surgery is highly operator dependant with a lengthy
procedural time causing fatigue to surgeon and risks to patients such as injury
to organs, infection, bleeding, and complications of anesthesia. To mitigate
such risks, real-time systems are desired to be developed that can provide
intra-operative guidance to surgeons. For example, an automated system for tool
localization, tool (or tissue) tracking, and depth estimation can enable a
clear understanding of surgical scenes preventing miscalculations during
surgical procedures. In this work, we present a systematic review of recent
machine learning-based approaches including surgical tool localization,
segmentation, tracking, and 3D scene perception. Furthermore, we provide a
detailed overview of publicly available benchmark datasets widely used for
surgical navigation tasks. While recent deep learning architectures have shown
promising results, there are still several open research problems such as a
lack of annotated datasets, the presence of artifacts in surgical scenes, and
non-textured surfaces that hinder 3D reconstruction of the anatomical
structures. Based on our comprehensive review, we present a discussion on
current gaps and needed steps to improve the adaptation of technology in
surgery.Comment: This paper is to be submitted to International journal of computer
visio
Intelligent Robotic Perception Systems
Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics
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