27,459 research outputs found
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization,
mapping and navigation. These sensors, however, cannot detect transparent
surfaces or measure the full occupancy of complex objects such as tables. Deep
Neural Networks have recently been proposed to overcome this limitation by
learning to estimate object occupancy. These estimates are nevertheless subject
to uncertainty, making the evaluation of their confidence an important issue
for these measures to be useful for autonomous navigation and mapping. In this
work we approach the problem from two sides. First we discuss uncertainty
estimation in deep models, proposing a solution based on a fully convolutional
neural network. The proposed architecture is not restricted by the assumption
that the uncertainty follows a Gaussian model, as in the case of many popular
solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout.
We present results showing that uncertainty over obstacle distances is actually
better modeled with a Laplace distribution. Then, we propose a novel approach
to build maps based on Deep Neural Network uncertainty models. In particular,
we present an algorithm to build a map that includes information over obstacle
distance estimates while taking into account the level of uncertainty in each
estimate. We show how the constructed map can be used to increase global
navigation safety by planning trajectories which avoid areas of high
uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference
on Humanoid Robots (Humanoids)
Towards a TCT-inspired electronics concept inventory
This study reports on the initial work on the use of Threshold Concept Theory (TCT) to develop a threshold-concept inventory â a catalogue of the important concepts that underlie electronics and electrical engineering (EE) â and an assessment tool â to investigate the depth of student understanding of threshold and related concepts, independent of studentsâ numerical ability and knowledge mimicry in the first-year course in electrical engineering. This is both challenging and important for several reasons: there is a known issue with student retention (Tsividis, 1998; 2009); the discipline is relatively hard for students because it concerns invisible phenomena; and finally it is one that demands deep understanding from the very start (Scott, Harlow, Peter, and Cowie, 2010). Although the focus of this research was on electronic circuits, findings regarding teaching and learning of threshold concepts (TCs) will inform lecturers in three other disciplines who are part of our project on threshold concepts
PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds
Grasping for novel objects is important for robot manipulation in
unstructured environments. Most of current works require a grasp sampling
process to obtain grasp candidates, combined with local feature extractor using
deep learning. This pipeline is time-costly, expecially when grasp points are
sparse such as at the edge of a bowl. In this paper, we propose an end-to-end
approach to directly predict the poses, categories and scores (qualities) of
all the grasps. It takes the whole sparse point clouds as the input and
requires no sampling or search process. Moreover, to generate training data of
multi-object scene, we propose a fast multi-object grasp detection algorithm
based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB
object set, 23.7k grasps) and a multi-object dataset (20k point clouds with
annotations and masks) are generated. A PointNet++ based network combined with
multi-mask loss is introduced to deal with different training points. The whole
weight size of our network is only about 11.6M, which takes about 102ms for a
whole prediction process using a GeForce 840M GPU. Our experiment shows our
work get 71.43% success rate and 91.60% completion rate, which performs better
than current state-of-art works.Comment: Accepted at the International Conference on Robotics and Automation
(ICRA) 202
SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes
In this work, we present SynTable, a unified and flexible Python-based
dataset generator built using NVIDIA's Isaac Sim Replicator Composer for
generating high-quality synthetic datasets for unseen object amodal instance
segmentation of cluttered tabletop scenes. Our dataset generation tool can
render a complex 3D scene containing object meshes, materials, textures,
lighting, and backgrounds. Metadata, such as modal and amodal instance
segmentation masks, occlusion masks, depth maps, bounding boxes, and material
properties, can be generated to automatically annotate the scene according to
the users' requirements. Our tool eliminates the need for manual labeling in
the dataset generation process while ensuring the quality and accuracy of the
dataset. In this work, we discuss our design goals, framework architecture, and
the performance of our tool. We demonstrate the use of a sample dataset
generated using SynTable by ray tracing for training a state-of-the-art model,
UOAIS-Net. The results show significantly improved performance in Sim-to-Real
transfer when evaluated on the OSD-Amodal dataset. We offer this tool as an
open-source, easy-to-use, photorealistic dataset generator for advancing
research in deep learning and synthetic data generation.Comment: Version
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