150,190 research outputs found
Enhancing Graph Representation of the Environment through Local and Cloud Computation
Enriching the robot representation of the operational environment is a
challenging task that aims at bridging the gap between low-level sensor
readings and high-level semantic understanding. Having a rich representation
often requires computationally demanding architectures and pure point cloud
based detection systems that struggle when dealing with everyday objects that
have to be handled by the robot. To overcome these issues, we propose a
graph-based representation that addresses this gap by providing a semantic
representation of robot environments from multiple sources. In fact, to acquire
information from the environment, the framework combines classical computer
vision tools with modern computer vision cloud services, ensuring computational
feasibility on onboard hardware. By incorporating an ontology hierarchy with
over 800 object classes, the framework achieves cross-domain adaptability,
eliminating the need for environment-specific tools. The proposed approach
allows us to handle also small objects and integrate them into the semantic
representation of the environment. The approach is implemented in the Robot
Operating System (ROS) using the RViz visualizer for environment
representation. This work is a first step towards the development of a
general-purpose framework, to facilitate intuitive interaction and navigation
across different domains.Comment: 5 pages, 4 figure
Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision
We address one of the main challenges towards autonomous quadrotor flight in
complex environments, which is flight through narrow gaps. While previous works
relied on off-board localization systems or on accurate prior knowledge of the
gap position and orientation, we rely solely on onboard sensing and computing
and estimate the full state by fusing gap detection from a single onboard
camera with an IMU. This problem is challenging for two reasons: (i) the
quadrotor pose uncertainty with respect to the gap increases quadratically with
the distance from the gap; (ii) the quadrotor has to actively control its
orientation towards the gap to enable state estimation (i.e., active vision).
We solve this problem by generating a trajectory that considers geometric,
dynamic, and perception constraints: during the approach maneuver, the
quadrotor always faces the gap to allow state estimation, while respecting the
vehicle dynamics; during the traverse through the gap, the distance of the
quadrotor to the edges of the gap is maximized. Furthermore, we replan the
trajectory during its execution to cope with the varying uncertainty of the
state estimate. We successfully evaluate and demonstrate the proposed approach
in many real experiments. To the best of our knowledge, this is the first work
that addresses and achieves autonomous, aggressive flight through narrow gaps
using only onboard sensing and computing and without prior knowledge of the
pose of the gap
Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation
There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks
Obstacle detection is a safety-critical problem in robot navigation, where
stereo matching is a popular vision-based approach. While deep neural networks
have shown impressive results in computer vision, most of the previous obstacle
detection works only leverage traditional stereo matching techniques to meet
the computational constraints for real-time feedback. This paper proposes a
computationally efficient method that leverages a deep neural network to detect
occupancy from stereo images directly. Instead of learning the point cloud
correspondence from the stereo data, our approach extracts the compact obstacle
distribution based on volumetric representations. In addition, we prune the
computation of safety irrelevant spaces in a coarse-to-fine manner based on
octrees generated by the decoder. As a result, we achieve real-time performance
on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles
accurately in the range of 32 meters and achieves better IoU (Intersection over
Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of
the state-of-the-art stereo model. Furthermore, we validate our method's
robustness and real-world feasibility through autonomous navigation experiments
with a real robot. Hence, our work contributes toward closing the gap between
the stereo-based system in robot perception and state-of-the-art stereo models
in computer vision. To counter the scarcity of high-quality real-world indoor
stereo datasets, we collect a 1.36 hours stereo dataset with a Jackal robot
which is used to fine-tune our model. The dataset, the code, and more
visualizations are available at https://lhy.xyz/stereovoxelnet
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