21,320 research outputs found
Active vision-based localization for robots in a home-tour scenario
Self-Localization is a crucial task for mobile robots. It is not only a requirement
for auto navigation but also provides contextual information to support
human robot interaction (HRI). In this paper we present an active vision-based
localization method for integration in a complex robot system to work in human
interaction scenarios (e.g. home-tour) in a real world apartment. The holistic
features used are robust to illumination and structural changes in the scene. The
system uses only a single pan-tilt camera shared between different vision applications
running in parallel to reduce the number of sensors. Additional information
from other modalities (like laser scanners) can be used, profiting of an integration
into an existing system. The camera view can be actively adapted and the
evaluation showed that different rooms can be discerned
Ten years of cooperation between mobile robots and sensor networks
This paper presents an overview of the work carried out by
the Group of Robotics, Vision and Control (GRVC) at the
University of Seville on the cooperation between mobile
robots and sensor networks. The GRVC, led by Professor
Anibal Ollero, has been working over the last ten years on
techniques where robots and sensor networks exploit
synergies and collaborate tightly, developing numerous
research projects on the topic. In this paper, based on our
research, we introduce what we consider some relevant
challenges when combining sensor networks with mobile
robots. Then, we describe our developed techniques and
main results for these challenges. In particular, the paper
focuses on autonomous self-deployment of sensor networks;
cooperative localization and tracking; self-localization
and mapping; and large-scale scenarios. Extensive
experimental results and lessons learnt are also discussed
in the paper
Vision Based Localization for Multiple UAVs and Mobile Robots
Master'sMASTER OF ENGINEERIN
Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Localization is a key requirement for mobile robot autonomy and human-robot
interaction. Vision-based localization is accurate and flexible, however, it
incurs a high computational burden which limits its application on many
resource-constrained platforms. In this paper, we address the problem of
performing real-time localization in large-scale 3D point cloud maps of
ever-growing size. While most systems using multi-modal information reduce
localization time by employing side-channel information in a coarse manner (eg.
WiFi for a rough prior position estimate), we propose to inter-weave the map
with rich sensory data. This multi-modal approach achieves two key goals
simultaneously. First, it enables us to harness additional sensory data to
localise against a map covering a vast area in real-time; and secondly, it also
allows us to roughly localise devices which are not equipped with a camera. The
key to our approach is a localization policy based on a sequential Monte Carlo
estimator. The localiser uses this policy to attempt point-matching only in
nodes where it is likely to succeed, significantly increasing the efficiency of
the localization process. The proposed multi-modal localization system is
evaluated extensively in a large museum building. The results show that our
multi-modal approach not only increases the localization accuracy but
significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
Learning Matchable Image Transformations for Long-term Metric Visual Localization
Long-term metric self-localization is an essential capability of autonomous
mobile robots, but remains challenging for vision-based systems due to
appearance changes caused by lighting, weather, or seasonal variations. While
experience-based mapping has proven to be an effective technique for bridging
the `appearance gap,' the number of experiences required for reliable metric
localization over days or months can be very large, and methods for reducing
the necessary number of experiences are needed for this approach to scale.
Taking inspiration from color constancy theory, we learn a nonlinear
RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature
matches for images captured under different lighting and weather conditions,
and use it as a pre-processing step in a conventional single-experience
localization pipeline to improve its robustness to appearance change. We train
this mapping by approximating the target non-differentiable localization
pipeline with a deep neural network, and find that incorporating a learned
low-dimensional context feature can further improve cross-appearance feature
matching. Using synthetic and real-world datasets, we demonstrate substantial
improvements in localization performance across day-night cycles, enabling
continuous metric localization over a 30-hour period using a single mapping
experience, and allowing experience-based localization to scale to long
deployments with dramatically reduced data requirements.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'20), Paris,
France, May 31-June 4, 202
Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris
Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar
Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang
dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai
325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi
Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang
diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan
pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan
hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur
penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan
Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang
mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap.
Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat
hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara
minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif
sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin
tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar
dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam
diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh
itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam
membuat kajian yang akan datang
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