335 research outputs found
Probabilistic Surfel Fusion for Dense LiDAR Mapping
With the recent development of high-end LiDARs, more and more systems are
able to continuously map the environment while moving and producing spatially
redundant information. However, none of the previous approaches were able to
effectively exploit this redundancy in a dense LiDAR mapping problem. In this
paper, we present a new approach for dense LiDAR mapping using probabilistic
surfel fusion. The proposed system is capable of reconstructing a high-quality
dense surface element (surfel) map from spatially redundant multiple views.
This is achieved by a proposed probabilistic surfel fusion along with a
geometry considered data association. The proposed surfel data association
method considers surface resolution as well as high measurement uncertainty
along its beam direction which enables the mapping system to be able to control
surface resolution without introducing spatial digitization. The proposed
fusion method successfully suppresses the map noise level by considering
measurement noise caused by laser beam incident angle and depth distance in a
Bayesian filtering framework. Experimental results with simulated and real data
for the dense surfel mapping prove the ability of the proposed method to
accurately find the canonical form of the environment without further
post-processing.Comment: Accepted in Multiview Relationships in 3D Data 2017 (IEEE
International Conference on Computer Vision Workshops
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Map-centric SLAM is emerging as an alternative of conventional graph-based
SLAM for its accuracy and efficiency in long-term mapping problems. However, in
map-centric SLAM, the process of loop closure differs from that of conventional
SLAM and the result of incorrect loop closure is more destructive and is not
reversible. In this paper, we present a tightly coupled photogeometric metric
localization for the loop closure problem in map-centric SLAM. In particular,
our method combines complementary constraints from LiDAR and camera sensors,
and validates loop closure candidates with sequential observations. The
proposed method provides a visual evidence-based outlier rejection where
failures caused by either place recognition or localization outliers can be
effectively removed. We demonstrate the proposed method is not only more
accurate than the conventional global ICP methods but is also robust to
incorrect initial pose guesses.Comment: To Appear in IEEE ROBOTICS AND AUTOMATION LETTERS, ACCEPTED JANUARY
201
K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings
Numerous datasets have been proposed to combat the spread of online hate.
Despite these efforts, a majority of these resources are English-centric,
primarily focusing on overt forms of hate. This research gap calls for
developing high-quality corpora in diverse languages that also encapsulate more
subtle hate expressions. This study introduces K-HATERS, a new corpus for hate
speech detection in Korean, comprising approximately 192K news comments with
target-specific offensiveness ratings. This resource is the largest offensive
language corpus in Korean and is the first to offer target-specific ratings on
a three-point Likert scale, enabling the detection of hate expressions in
Korean across varying degrees of offensiveness. We conduct experiments showing
the effectiveness of the proposed corpus, including a comparison with existing
datasets. Additionally, to address potential noise and bias in human
annotations, we explore a novel idea of adopting the Cognitive Reflection Test,
which is widely used in social science for assessing an individual's cognitive
ability, as a proxy of labeling quality. Findings indicate that annotations
from individuals with the lowest test scores tend to yield detection models
that make biased predictions toward specific target groups and are less
accurate. This study contributes to the NLP research on hate speech detection
and resource construction. The code and dataset can be accessed at
https://github.com/ssu-humane/K-HATERS.Comment: 15 pages, EMNLP 2023 (Findings
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