1,165 research outputs found
Detection-by-Localization: Maintenance-Free Change Object Detector
Recent researches demonstrate that self-localization performance is a very
useful measure of likelihood-of-change (LoC) for change detection. In this
paper, this "detection-by-localization" scheme is studied in a novel
generalized task of object-level change detection. In our framework, a given
query image is segmented into object-level subimages (termed "scene parts"),
which are then converted to subimage-level pixel-wise LoC maps via the
detection-by-localization scheme. Our approach models a self-localization
system as a ranking function, outputting a ranked list of reference images,
without requiring relevance score. Thanks to this new setting, we can
generalize our approach to a broad class of self-localization systems. Our
ranking based self-localization model allows to fuse self-localization results
from different modalities via an unsupervised rank fusion derived from a field
of multi-modal information retrieval (MMR).Comment: 7 pages, 3 figures, Technical repor
ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping
In this paper, a novel solution is introduced for visual Simultaneous
Localization and Mapping (vSLAM) that is built up of Deep Learning components.
The proposed architecture is a highly modular framework in which each component
offers state of the art results in their respective fields of vision-based deep
learning solutions. The paper shows that with the synergic integration of these
individual building blocks, a functioning and efficient all-through deep neural
(ATDN) vSLAM system can be created. The Embedding Distance Loss function is
introduced and using it the ATDN architecture is trained. The resulting system
managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a
subset of the KITTI dataset. The proposed architecture can be used for
efficient and low-latency autonomous driving (AD) aiding database creation as
well as a basis for autonomous vehicle (AV) control.Comment: Published in Periodica Polytechnica Electrical Engineering 11 page
LiDAR-Based Place Recognition For Autonomous Driving: A Survey
LiDAR-based place recognition (LPR) plays a pivotal role in autonomous
driving, which assists Simultaneous Localization and Mapping (SLAM) systems in
reducing accumulated errors and achieving reliable localization. However,
existing reviews predominantly concentrate on visual place recognition (VPR)
methods. Despite the recent remarkable progress in LPR, to the best of our
knowledge, there is no dedicated systematic review in this area. This paper
bridges the gap by providing a comprehensive review of place recognition
methods employing LiDAR sensors, thus facilitating and encouraging further
research. We commence by delving into the problem formulation of place
recognition, exploring existing challenges, and describing relations to
previous surveys. Subsequently, we conduct an in-depth review of related
research, which offers detailed classifications, strengths and weaknesses, and
architectures. Finally, we summarize existing datasets, commonly used
evaluation metrics, and comprehensive evaluation results from various methods
on public datasets. This paper can serve as a valuable tutorial for newcomers
entering the field of place recognition and for researchers interested in
long-term robot localization. We pledge to maintain an up-to-date project on
our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table
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