40 research outputs found
l-dyno: framework to learn consistent visual features using robot's motion
Historically, feature-based approaches have been used extensively for
camera-based robot perception tasks such as localization, mapping, tracking,
and others. Several of these approaches also combine other sensors (inertial
sensing, for example) to perform combined state estimation. Our work rethinks
this approach; we present a representation learning mechanism that identifies
visual features that best correspond to robot motion as estimated by an
external signal. Specifically, we utilize the robot's transformations through
an external signal (inertial sensing, for example) and give attention to image
space that is most consistent with the external signal. We use a pairwise
consistency metric as a representation to keep the visual features consistent
through a sequence with the robot's relative pose transformations. This
approach enables us to incorporate information from the robot's perspective
instead of solely relying on the image attributes. We evaluate our approach on
real-world datasets such as KITTI & EuRoC and compare the refined features with
existing feature descriptors. We also evaluate our method using our real robot
experiment. We notice an average of 49% reduction in the image search space
without compromising the trajectory estimation accuracy. Our method reduces the
execution time of visual odometry by 4.3% and also reduces reprojection errors.
We demonstrate the need to select only the most important features and show the
competitiveness using various feature detection baselines.Comment: 7 pages, 6 figure
PQM: A Point Quality Evaluation Metric for Dense Maps
LiDAR-based mapping/reconstruction are important for various applications,
but evaluating the quality of the dense maps they produce is challenging. The
current methods have limitations, including the inability to capture
completeness, structural information, and local variations in error. In this
paper, we propose a novel point quality evaluation metric (PQM) that consists
of four sub-metrics to provide a more comprehensive evaluation of point cloud
quality. The completeness sub-metric evaluates the proportion of missing data,
the artifact score sub-metric recognizes and characterizes artifacts, the
accuracy sub-metric measures registration accuracy, and the resolution
sub-metric quantifies point cloud density. Through an ablation study using a
prototype dataset, we demonstrate the effectiveness of each of the sub-metrics
and compare them to popular point cloud distance measures. Using three LiDAR
SLAM systems to generate maps, we evaluate their output map quality and
demonstrate the metrics robustness to noise and artifacts. Our implementation
of PQM, datasets and detailed documentation on how to integrate with your
custom dense mapping pipeline can be found at github.com/droneslab/pq
DIOR: Dataset for Indoor-Outdoor Reidentification -- Long Range 3D/2D Skeleton Gait Collection Pipeline, Semi-Automated Gait Keypoint Labeling and Baseline Evaluation Methods
In recent times, there is an increased interest in the identification and
re-identification of people at long distances, such as from rooftop cameras,
UAV cameras, street cams, and others. Such recognition needs to go beyond face
and use whole-body markers such as gait. However, datasets to train and test
such recognition algorithms are not widely prevalent, and fewer are labeled.
This paper introduces DIOR -- a framework for data collection, semi-automated
annotation, and also provides a dataset with 14 subjects and 1.649 million RGB
frames with 3D/2D skeleton gait labels, including 200 thousands frames from a
long range camera. Our approach leverages advanced 3D computer vision
techniques to attain pixel-level accuracy in indoor settings with motion
capture systems. Additionally, for outdoor long-range settings, we remove the
dependency on motion capture systems and adopt a low-cost, hybrid 3D computer
vision and learning pipeline with only 4 low-cost RGB cameras, successfully
achieving precise skeleton labeling on far-away subjects, even when their
height is limited to a mere 20-25 pixels within an RGB frame. On publication,
we will make our pipeline open for others to use
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning
Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and
fast travel in urban and suburban hubs. These UAM aircraft are conceived to
operate from small airports called vertiports each comprising multiple
take-off/landing and battery-recharging spots. Since they might be situated in
dense urban areas and need to handle many aircraft landings and take-offs each
hour, managing this schedule in real-time becomes challenging for a traditional
air-traffic controller but instead calls for an automated solution. This paper
provides a novel approach to this problem of Urban Air Mobility - Vertiport
Schedule Management (UAM-VSM), which leverages graph reinforcement learning to
generate decision-support policies. Here the designated physical spots within
the vertiport's airspace and the vehicles being managed are represented as two
separate graphs, with feature extraction performed through a graph
convolutional network (GCN). Extracted features are passed onto perceptron
layers to decide actions such as continue to hover or cruise, continue idling
or take-off, or land on an allocated vertiport spot. Performance is measured
based on delays, safety (no. of collisions) and battery consumption. Through
realistic simulations in AirSim applied to scaled down multi-rotor vehicles,
our results demonstrate the suitability of using graph reinforcement learning
to solve the UAM-VSM problem and its superiority to basic reinforcement
learning (with graph embeddings) or random choice baselines.Comment: Accepted for presentation in proceedings of IEEE/RSJ International
Conference on Intelligent Robots and Systems 202
Enabling Automated, Rich, and Versatile Data Management for Android Apps with BlueMountain
Abstract Today's mobile apps often leverage cloud services to manage their own data as well as user data, enabling many desired features such as backup and sharing. However, this comes at a cost; developers have to manually craft their logic and potentially repeat a similar process for different cloud providers. In addition, users are restricted to the design choices made by developers; for example, once a developer releases an app that uses a particular cloud service, it is impossible for a user to later customize the app and choose a different service. In this paper, we explore the design space of an app instrumentation tool that automatically integrates cloud storage services for Android apps. Our goal is to allow developers to treat all storage operations as local operations, and automatically enable cloud features customized for individual needs of users and developers. We discuss various scenarios that can benefit from such an automated tool, challenges associated with the development of it, and our ideas to address these challenges
PyPose: A Library for Robot Learning with Physics-based Optimization
Deep learning has had remarkable success in robotic perception, but its
data-centric nature suffers when it comes to generalizing to ever-changing
environments. By contrast, physics-based optimization generalizes better, but
it does not perform as well in complicated tasks due to the lack of high-level
semantic information and the reliance on manual parametric tuning. To take
advantage of these two complementary worlds, we present PyPose: a
robotics-oriented, PyTorch-based library that combines deep perceptual models
with physics-based optimization techniques. Our design goal for PyPose is to
make it user-friendly, efficient, and interpretable with a tidy and
well-organized architecture. Using an imperative style interface, it can be
easily integrated into real-world robotic applications. Besides, it supports
parallel computing of any order gradients of Lie groups and Lie algebras and
-order optimizers, such as trust region methods. Experiments
show that PyPose achieves 3-20 speedup in computation compared to
state-of-the-art libraries. To boost future research, we provide concrete
examples across several fields of robotics, including SLAM, inertial
navigation, planning, and control
PyPose v0.6: The Imperative Programming Interface for Robotics
PyPose is an open-source library for robot learning. It combines a
learning-based approach with physics-based optimization, which enables seamless
end-to-end robot learning. It has been used in many tasks due to its
meticulously designed application programming interface (API) and efficient
implementation. From its initial launch in early 2022, PyPose has experienced
significant enhancements, incorporating a wide variety of new features into its
platform. To satisfy the growing demand for understanding and utilizing the
library and reduce the learning curve of new users, we present the fundamental
design principle of the imperative programming interface, and showcase the
flexible usage of diverse functionalities and modules using an extremely simple
Dubins car example. We also demonstrate that the PyPose can be easily used to
navigate a real quadruped robot with a few lines of code
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Boundary Detection Using Actuated Sensor Networks
We present an algorithm that detects and traces a contour of a scalar field. A set of static sensor nodes are deployed in a given area. The algorithm causes a mobile sensor node to approach a given contour. The algorithm uses local communication between the mobile node and its immediate neighbors only. Also, the path generated by the mobile node is near optimal when the static nodes are deployed at reasonable densities (avg. degree of about six)