280 research outputs found
Spatially regularized T1 estimation from variable flip angles MRI
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134899/1/mp2747.pd
Multi-Agent Combinatorial Path Finding with Heterogeneous Task Duration
Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for
multiple agents from their initial locations to destinations, visiting a set of
intermediate target locations in the middle of the paths, while minimizing the
sum of arrival times. While a few approaches have been developed to handle
MCPF, most of them simply direct the agent to visit the targets without
considering the task duration, i.e., the amount of time needed for an agent to
execute the task (such as picking an item) at a target location. MCPF is
NP-hard to solve to optimality, and the inclusion of task duration further
complicates the problem. This paper investigates heterogeneous task duration,
where the duration can be different with respect to both the agents and
targets. We develop two methods, where the first method post-processes the
paths planned by any MCPF planner to include the task duration and has no
solution optimality guarantee; and the second method considers task duration
during planning and is able to ensure solution optimality. The numerical and
simulation results show that our methods can handle up to 20 agents and 50
targets in the presence of task duration, and can execute the paths subject to
robot motion disturbance
Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation
LiDAR point cloud semantic segmentation enables the robots to obtain
fine-grained semantic information of the surrounding environment. Recently,
many works project the point cloud onto the 2D image and adopt the 2D
Convolutional Neural Networks (CNNs) or vision transformer for LiDAR point
cloud semantic segmentation. However, since more than one point can be
projected onto the same 2D position but only one point can be preserved, the
previous 2D image-based segmentation methods suffer from inevitable quantized
information loss. To avoid quantized information loss, in this paper, we
propose a novel spherical frustum structure. The points projected onto the same
2D position are preserved in the spherical frustums. Moreover, we propose a
memory-efficient hash-based representation of spherical frustums. Through the
hash-based representation, we propose the Spherical Frustum sparse Convolution
(SFC) and Frustum Fast Point Sampling (F2PS) to convolve and sample the points
stored in spherical frustums respectively. Finally, we present the Spherical
Frustum sparse Convolution Network (SFCNet) to adopt 2D CNNs for LiDAR point
cloud semantic segmentation without quantized information loss. Extensive
experiments on the SemanticKITTI and nuScenes datasets demonstrate that our
SFCNet outperforms the 2D image-based semantic segmentation methods based on
conventional spherical projection. The source code will be released later.Comment: 17 pages, 10 figures, under revie
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