196 research outputs found
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
This paper proposes \textit{Contour Context}, a simple, effective, and
efficient topological loop closure detection pipeline with accurate 3-DoF
metric pose estimation, targeting the urban utonomous driving scenario. We
interpret the Cartesian birds' eye view (BEV) image projected from 3D LiDAR
points as layered distribution of structures. To recover elevation information
from BEVs, we slice them at different heights, and connected pixels at each
level will form contours. Each contour is parameterized by abstract
information, e.g., pixel count, center position, covariance, and mean height.
The similarity of two BEVs is calculated in sequential discrete and continuous
steps. The first step considers the geometric consensus of graph-like
constellations formed by contours in particular localities. The second step
models the majority of contours as a 2.5D Gaussian mixture model, which is used
to calculate correlation and optimize relative transform in continuous space. A
retrieval key is designed to accelerate the search of a database indexed by
layered KD-trees. We validate the efficacy of our method by comparing it with
recent works on public datasets.Comment: 7 pages, 7 figures, accepted by ICRA 202
Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots
Diversity is a long-studied topic in information retrieval that usually
refers to the requirement that retrieved results should be non-repetitive and
cover different aspects. In a conversational setting, an additional dimension
of diversity matters: an engaging response generation system should be able to
output responses that are diverse and interesting. Sequence-to-sequence
(Seq2Seq) models have been shown to be very effective for response generation.
However, dialogue responses generated by Seq2Seq models tend to have low
diversity. In this paper, we review known sources and existing approaches to
this low-diversity problem. We also identify a source of low diversity that has
been little studied so far, namely model over-confidence. We sketch several
directions for tackling model over-confidence and, hence, the low-diversity
problem, including confidence penalties and label smoothing
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss
Sequence-to-Sequence (Seq2Seq) models have achieved encouraging performance
on the dialogue response generation task. However, existing Seq2Seq-based
response generation methods suffer from a low-diversity problem: they
frequently generate generic responses, which make the conversation less
interesting. In this paper, we address the low-diversity problem by
investigating its connection with model over-confidence reflected in predicted
distributions. Specifically, we first analyze the influence of the commonly
used Cross-Entropy (CE) loss function, and find that the CE loss function
prefers high-frequency tokens, which results in low-diversity responses. We
then propose a Frequency-Aware Cross-Entropy (FACE) loss function that improves
over the CE loss function by incorporating a weighting mechanism conditioned on
token frequency. Extensive experiments on benchmark datasets show that the FACE
loss function is able to substantially improve the diversity of existing
state-of-the-art Seq2Seq response generation methods, in terms of both
automatic and human evaluations.Comment: Will appear at The Web Conference 201
G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model
This study introduces a novel framework, G3Reg, for fast and robust global
registration of LiDAR point clouds. In contrast to conventional complex
keypoints and descriptors, we extract fundamental geometric primitives
including planes, clusters, and lines (PCL) from the raw point cloud to obtain
low-level semantic segments. Each segment is formulated as a unified Gaussian
Ellipsoid Model (GEM) by employing a probability ellipsoid to ensure the ground
truth centers are encompassed with a certain degree of probability. Utilizing
these GEMs, we then present a distrust-and-verify scheme based on a Pyramid
Compatibility Graph for Global Registration (PAGOR). Specifically, we establish
an upper bound, which can be traversed based on the confidence level for
compatibility testing to construct the pyramid graph. Gradually, we solve
multiple maximum cliques (MAC) for each level of the graph, generating numerous
transformation candidates. In the verification phase, we adopt a precise and
efficient metric for point cloud alignment quality, founded on geometric
primitives, to identify the optimal candidate. The performance of the algorithm
is extensively validated on three publicly available datasets and a
self-collected multi-session dataset, without changing any parameter settings
in the experimental evaluation. The results exhibit superior robustness and
real-time performance of the G3Reg framework compared to state-of-the-art
methods. Furthermore, we demonstrate the potential for integrating individual
GEM and PAGOR components into other algorithmic frameworks to enhance their
efficacy. To advance further research and promote community understanding, we
have publicly shared the source code.Comment: Under revie
FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes
3D coverage path planning for UAVs is a crucial problem in diverse practical
applications. However, existing methods have shown unsatisfactory system
simplicity, computation efficiency, and path quality in large and complex
scenes. To address these challenges, we propose FC-Planner, a skeleton-guided
planning framework that can achieve fast aerial coverage of complex 3D scenes
without pre-processing. We decompose the scene into several simple subspaces by
a skeleton-based space decomposition (SSD). Additionally, the skeleton guides
us to effortlessly determine free space. We utilize the skeleton to efficiently
generate a minimal set of specialized and informative viewpoints for complete
coverage. Based on SSD, a hierarchical planner effectively divides the large
planning problem into independent sub-problems, enabling parallel planning for
each subspace. The carefully designed global and local planning strategies are
then incorporated to guarantee both high quality and efficiency in path
generation. We conduct extensive benchmark and real-world tests, where
FC-Planner computes over 10 times faster compared to state-of-the-art methods
with shorter path and more complete coverage. The source code will be open at
https://github.com/HKUST-Aerial-Robotics/FC-Planner.Comment: Submitted to ICRA2024. 6 Pages, 6 Figures, 3 Tables. Code:
https://github.com/HKUST-Aerial-Robotics/FC-Planner. Video:
https://www.bilibili.com/video/BV1h84y1D7u5/?spm_id_from=333.999.0.0&vd_source=0af61c122e5e37c944053b57e313025
Interactions between aerosol organic components and liquid water content during haze episodes in Beijing
Aerosol liquid water (ALW) is ubiquitous in ambient aerosol and plays an important role in the formation of both aerosol organics and inorganics. To investigate the interactions between ALW and aerosol organics during haze formation and evolution, ALW was modelled based on long-term measurement of submicron aerosol composition in different seasons in Beijing. ALW contributed by aerosol inorganics (ALW(inorg)) was modelled by ISORROPIA II, and ALW contributed by organics (ALW(org)) was estimated with kappa-Kohler theory, where the real-time hygroscopicity parameter of the organics (kappa(org)) was calculated from the real-time organic oxygen-to-carbon ratio (O/C). Overall particle hygroscopicity (kappa(total)) was computed by weighting component hygroscopicity parameters based on their volume fractions in the mixture. We found that ALW(org), which is often neglected in traditional ALW modelling, contributes a significant fraction (18 %-32 %) to the total ALW in Beijing. The ALW(org) fraction is largest on the cleanest days when both the organic fraction and kappa(org) are relatively high. The large variation in O/C, from 0.2 to 1.3, indicates the wide variety of organic components. This emphasizes the necessity of using real-time kappa(org), instead of fixed kappa(org), to calculate ALW(org) in Beijing. The significant variation in K org (calculated from O/C), together with highly variable organic or inorganic volume fractions, leads to a wide range of kappa(total) (between 0.20 and 0.45), which has a great impact on water uptake. The variation in organic O/C, or derived K org , was found to be influenced by temperature (T), ALW, and aerosol mass concentrations, among which T and ALW both have promoting effects on O/C. During high-ALW haze episodes, although the organic fraction decreases rapidly, O/C and derived K org increase with the increase in ALW, suggesting the formation of more soluble organics via heterogeneous uptake or aqueous processes. A positive feedback loop is thus formed: during high-ALW episodes, increasing kappa(org), together with decreasing particle organic fraction (or increasing particle inorganic fraction), increases kappa(total), and thus further promotes the ability of particles to uptake water.Peer reviewe
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