2,656 research outputs found
Large-scale Point Cloud Registration Based on Graph Matching Optimization
Point Clouds Registration is a fundamental and challenging problem in 3D
computer vision. It has been shown that the isometric transformation is an
essential property in rigid point cloud registration, but the existing methods
only utilize it in the outlier rejection stage. In this paper, we emphasize
that the isometric transformation is also important in the feature learning
stage for improving registration quality. We propose a \underline{G}raph
\underline{M}atching \underline{O}ptimization based \underline{Net}work
(denoted as GMONet for short), which utilizes the graph matching method to
explicitly exert the isometry preserving constraints in the point feature
learning stage to improve %refine the point representation. Specifically, we
%use exploit the partial graph matching constraint to enhance the overlap
region detection abilities of super points ( down-sampled key points)
and full graph matching to refine the registration accuracy at the fine-level
overlap region. Meanwhile, we leverage the mini-batch sampling to improve the
efficiency of the full graph matching optimization. Given high discriminative
point features in the evaluation stage, we utilize the RANSAC approach to
estimate the transformation between the scanned pairs. The proposed method has
been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The
experimental results show that our method achieves competitive performance
compared with the existing state-of-the-art baselines
X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments
Modern robotic systems are required to operate in challenging environments,
which demand reliable localization under challenging conditions. LiDAR-based
localization methods, such as the Iterative Closest Point (ICP) algorithm, can
suffer in geometrically uninformative environments that are known to
deteriorate point cloud registration performance and push optimization toward
divergence along weakly constrained directions. To overcome this issue, this
work proposes i) a robust fine-grained localizability detection module, and ii)
a localizability-aware constrained ICP optimization module, which couples with
the localizability detection module in a unified manner. The proposed
localizability detection is achieved by utilizing the correspondences between
the scan and the map to analyze the alignment strength against the principal
directions of the optimization as part of its fine-grained LiDAR localizability
analysis. In the second part, this localizability analysis is then integrated
into the scan-to-map point cloud registration to generate drift-free pose
updates by enforcing controlled updates or leaving the degenerate directions of
the optimization unchanged. The proposed method is thoroughly evaluated and
compared to state-of-the-art methods in simulated and real-world experiments,
demonstrating the performance and reliability improvement in LiDAR-challenging
environments. In all experiments, the proposed framework demonstrates accurate
and generalizable localizability detection and robust pose estimation without
environment-specific parameter tuning.Comment: 20 Pages, 20 Figures Submitted to IEEE Transactions On Robotics.
Supplementary Video: https://youtu.be/SviLl7q69aA Project Website:
https://sites.google.com/leggedrobotics.com/x-ic
La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
Vision-based safe autonomous UAV landing with panoramic sensors
The remarkable growth of unmanned aerial vehicles (UAVs) has also raised concerns about safety measures during their missions. To advance towards safer autonomous aerial robots, this thesis strives to develop a safe autonomous UAV landing solution, a vital part of every UAV operation. The project proposes a vision-based framework for monitoring the landing area by leveraging the omnidirectional view of a single panoramic camera pointing upwards to detect and localize any person within the landing zone. Then, it sends this information to approaching UAVs to either hover and wait or adaptively search for a more optimal position to land themselves. We utilize and fine-tune the YOLOv7 object detection model, an XGBooxt model for localizing nearby people, and the open-source ROS and PX4 frameworks for communications and drone control. We present both simulation and real-world indoor experimental results to demonstrate the capability of our methods
MOVES: Movable and Moving LiDAR Scene Segmentation in Label-Free settings using Static Reconstruction
Accurate static structure reconstruction and segmentation of non-stationary
objects is of vital importance for autonomous navigation applications. These
applications assume a LiDAR scan to consist of only static structures. In the
real world however, LiDAR scans consist of non-stationary dynamic structures -
moving and movable objects. Current solutions use segmentation information to
isolate and remove moving structures from LiDAR scan. This strategy fails in
several important use-cases where segmentation information is not available. In
such scenarios, moving objects and objects with high uncertainty in their
motion i.e. movable objects, may escape detection. This violates the above
assumption. We present MOVES, a novel GAN based adversarial model that segments
out moving as well as movable objects in the absence of segmentation
information. We achieve this by accurately transforming a dynamic LiDAR scan to
its corresponding static scan. This is obtained by replacing dynamic objects
and corresponding occlusions with static structures which were occluded by
dynamic objects. We leverage corresponding static-dynamic LiDAR pairs.Comment: 35 pages, 8 figures, 6 table
Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward
This chapter explores the complex realm of autonomous cars, analyzing their
fundamental components and operational characteristics. The initial phase of
the discussion is elucidating the internal mechanics of these automobiles,
encompassing the crucial involvement of sensors, artificial intelligence (AI)
identification systems, control mechanisms, and their integration with
cloud-based servers within the framework of the Internet of Things (IoT). It
delves into practical implementations of autonomous cars, emphasizing their
utilization in forecasting traffic patterns and transforming the dynamics of
transportation. The text also explores the topic of Robotic Process Automation
(RPA), illustrating the impact of autonomous cars on different businesses
through the automation of tasks. The primary focus of this investigation lies
in the realm of cybersecurity, specifically in the context of autonomous
vehicles. A comprehensive analysis will be conducted to explore various risk
management solutions aimed at protecting these vehicles from potential threats
including ethical, environmental, legal, professional, and social dimensions,
offering a comprehensive perspective on their societal implications. A
strategic plan for addressing the challenges and proposing strategies for
effectively traversing the complex terrain of autonomous car systems,
cybersecurity, hazards, and other concerns are some resources for acquiring an
understanding of the intricate realm of autonomous cars and their ramifications
in contemporary society, supported by a comprehensive compilation of resources
for additional investigation.
Keywords: RPA, Cyber Security, AV, Risk, Smart Car
SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views
In recent studies, the generalization of neural radiance fields for novel
view synthesis task has been widely explored. However, existing methods are
limited to objects and indoor scenes. In this work, we extend the
generalization task to outdoor scenes, trained only on object-level datasets.
This approach presents two challenges. Firstly, the significant distributional
shift between training and testing scenes leads to black artifacts in rendering
results. Secondly, viewpoint changes in outdoor scenes cause ghosting or
missing regions in rendered images. To address these challenges, we propose a
geometric correction module and an appearance correction module based on
multi-head attention mechanisms. We normalize rendered depth and combine it
with light direction as query in the attention mechanism. Our network
effectively corrects varying scene structures and geometric features in outdoor
scenes, generalizing well from object-level to unseen outdoor scenes.
Additionally, we use appearance correction module to correct appearance
features, preventing rendering artifacts like blank borders and ghosting due to
viewpoint changes. By combining these modules, our approach successfully
tackles the challenges of outdoor scene generalization, producing high-quality
rendering results. When evaluated on four datasets (Blender, DTU, LLFF,
Spaces), our network outperforms previous methods. Notably, compared to
MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from
0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes
Simultaneous Localization and Mapping and Tag-Based Navigation for Unmanned Aerial Vehicles
This paper presents navigation techniques for an Unmanned Aerial Vehicle (UAV) in a virtual simulation of an indoor environment using Simultaneous Localization and Mapping (SLAM) and April Tag markers to reach a target destination. In many cases, UAVs can access locations that are inaccessible to people or regular vehicles in indoor environments, making them valuable for surveillance purposes. This study employs the Robot Operating System (ROS) to simulate SLAM techniques using LIDAR and GMapping packages for UAV navigation in two different environments. In the Tag-based simulation, the input topic for April Tag in ROS is camera images, and the calibration of position with a tag is done through assigning a message to each ID and its marker image. On the other hand, navigation in SLAM was achieved using a global and local planner algorithm. For localization, an Adaptive Monte-Carlo Localization (AMCL) technique has been used to identify factors contributing to inconsistent mapping results, such as heavy computational load, grid mapping accuracy, and inadequate UAV localization. Furthermore, this study analyzed the April Tag-based navigation algorithm, which showed satisfactory outcomes due to its lighter computing requirements. It can be ascertained that by using ROS packages, the simulation of SLAM and Tag-based UAV navigation inside a building can be achieved.
 
Off the Radar: Uncertainty-Aware Radar Place Recognition with Introspective Querying and Map Maintenance
Localisation with Frequency-Modulated Continuous-Wave (FMCW) radar has gained
increasing interest due to its inherent resistance to challenging environments.
However, complex artefacts of the radar measurement process require appropriate
uncertainty estimation to ensure the safe and reliable application of this
promising sensor modality. In this work, we propose a multi-session map
management system which constructs the best maps for further localisation based
on learned variance properties in an embedding space. Using the same variance
properties, we also propose a new way to introspectively reject localisation
queries that are likely to be incorrect. For this, we apply robust noise-aware
metric learning, which both leverages the short-timescale variability of radar
data along a driven path (for data augmentation) and predicts the downstream
uncertainty in metric-space-based place recognition. We prove the effectiveness
of our method over extensive cross-validated tests of the Oxford Radar RobotCar
and MulRan dataset. In this, we outperform the current state-of-the-art in
radar place recognition and other uncertainty-aware methods when using only
single nearest-neighbour queries. We also show consistent performance increases
when rejecting queries based on uncertainty over a difficult test environment,
which we did not observe for a competing uncertainty-aware place recognition
system.Comment: 8 pages, 6 figure
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