Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: OctoPath: An OcTree Based Self-Supervised Learning Approach to Local
  Trajectory Planning for Mobile Robots   Autonomous mobile robots are usually faced with challenging situations when
driving in complex environments. Namely, they have to recognize the static and
dynamic obstacles, plan the driving path and execute their motion. For
addressing the issue of perception and path planning, in this paper, we
introduce OctoPath , which is an encoder-decoder deep neural network, trained
in a self-supervised manner to predict the local optimal trajectory for the
ego-vehicle. Using the discretization provided by a 3D octree environment
model, our approach reformulates trajectory prediction as a classification
problem with a configurable resolution. During training, OctoPath minimizes the
error between the predicted and the manually driven trajectories in a given
training dataset. This allows us to avoid the pitfall of regression-based
trajectory estimation, in which there is an infinite state space for the output
trajectory points. Environment sensing is performed using a 40-channel
mechanical LiDAR sensor, fused with an inertial measurement unit and wheels
odometry for state estimation. The experiments are performed both in simulation
and real-life, using our own developed GridSim simulator and RovisLab's
Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath
in different driving scenarios, both indoor and outdoor, while benchmarking our
system against a baseline hybrid A-Star algorithm and a regression-based
supervised learning method, as well as against a CNN learning-based optimal
path planning method.

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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
2
The categories of this paper are: math