4,291 research outputs found
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Autonomous control of underground mining vehicles using reactive navigation
Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine
Place Categorization and Semantic Mapping on a Mobile Robot
In this paper we focus on the challenging problem of place categorization and
semantic mapping on a robot without environment-specific training. Motivated by
their ongoing success in various visual recognition tasks, we build our system
upon a state-of-the-art convolutional network. We overcome its closed-set
limitations by complementing the network with a series of one-vs-all
classifiers that can learn to recognize new semantic classes online. Prior
domain knowledge is incorporated by embedding the classification system into a
Bayesian filter framework that also ensures temporal coherence. We evaluate the
classification accuracy of the system on a robot that maps a variety of places
on our campus in real-time. We show how semantic information can boost robotic
object detection performance and how the semantic map can be used to modulate
the robot's behaviour during navigation tasks. The system is made available to
the community as a ROS module
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is
one of the most challenging problem in mobile robotics and computer vision. In
this work we study the post-processing techniques applied to sparse 3D
point-cloud maps, obtained by feature-based vSLAM algorithms. Map
post-processing is split into 2 major steps: 1) noise and outlier removal and
2) upsampling. We evaluate different combinations of known algorithms for
outlier removing and upsampling on datasets of real indoor and outdoor
environments and identify the most promising combination. We further use it to
convert a point-cloud map, obtained by the real UAV performing indoor flight to
3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd
International Conference on Interactive Collaborative Robotics (ICR 2018)
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday
life. However, satellite-based localization (e.g., GPS) has limited accuracy
and can be unusable in dense urban areas and indoors. We introduce an
image-based global localization system that is accurate to a few millimeters
and performs reliable localization both indoors and outside. The key idea is to
capture and index distinctive local keypoints in ground textures. This is based
on the observation that ground textures including wood, carpet, tile, concrete,
and asphalt may look random and homogeneous, but all contain cracks, scratches,
or unique arrangements of fibers. These imperfections are persistent, and can
serve as local features. Our system incorporates a downward-facing camera to
capture the fine texture of the ground, together with an image processing
pipeline that locates the captured texture patch in a compact database
constructed offline. We demonstrate the capability of our system to robustly,
accurately, and quickly locate test images on various types of outdoor and
indoor ground surfaces
Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges
Alpha-N: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System
Alpha N A self-powered, wheel driven Automated Delivery Robot is presented in
this paper. The ADR is capable of navigating autonomously by detecting and
avoiding objects or obstacles in its path. It uses a vector map of the path and
calculates the shortest path by Grid Count Method of Dijkstra Algorithm.
Landmark determination with Radio Frequency Identification tags are placed in
the path for identification and verification of source and destination, and
also for the recalibration of the current position. On the other hand, an
Object Detection Module is built by Faster RCNN with VGGNet16 architecture for
supporting path planning by detecting and recognizing obstacles. The Path
Planning System is combined with the output of the GCM, the RFID Reading System
and also by the binary results of ODM. This PPS requires a minimum speed of 200
RPM and 75 seconds duration for the robot to successfully relocate its position
by reading an RFID tag. In the result analysis phase, the ODM exhibits an
accuracy of 83.75 percent, RRS shows 92.3 percent accuracy and the PPS
maintains an accuracy of 85.3 percent. Stacking all these 3 modules, the ADR is
built, tested and validated which shows significant improvement in terms of
performance and usability comparing with other service robots.Comment: 12 pages, 7 figures, To be appear in the proceedings of 12th Asian
Conference on Intelligent Information and Database Systems 23-26 March 2020
Phuket, Thailan
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