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Hao Liu

5 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2
  • last author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.RO4
  • cs.LG1
same name
  • Hao Liu — 23 papers, h 14
  • Hao Liu — 14 papers, h 8
  • Hao Liu — 14 papers, h 5
  • Hao Liu — 13 papers, h 12
  • Hao Liu — 10 papers, h 23
  • Hao Liu — 10 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedLocalization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses

3 citations · 5 across the 3 of their papers we have counts for

collaborators
Showing cs.ROShow all

4 papers · 1 filter

cs.RO2024★ 1 cited

Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

Yi Shen, Hao Liu, Chang Zhou +3

Unmanned Surface Vehicles (USVs) are pivotal in marine exploration, but their sensors' accuracy is compromised by the dynamic marine environment. Traditional calibration methods fa…

cs.RO2024

TD3 Based Collision Free Motion Planning for Robot Navigation

Hao Liu, Yi Shen, Chang Zhou +3

This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL…

cs.RO2024★ 3 cited

Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses

Yi Shen, Hao Liu, Xinxin Liu +3

The reduced cost and computational and calibration requirements of monocular cameras make them ideal positioning sensors for mobile robots, albeit at the expense of any meaningful…

cs.RO2024★ 1 cited

Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning

Hao Liu, Yi Shen, Wenjing Zhou +3

In order to solve the problem of frequent deceleration of unmanned vehicles when approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the Double Deep…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.