activity
20162022
most citedLearning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling

224 citations · 707 across the 23 of their papers we have counts for

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9 papers · 1 filter

cs.RO20211 cited

RadarLoc: Learning to Relocalize in FMCW Radar

Wei Wang, Pedro P. B. de Gusmo, Bo Yang +2

Relocalization is a fundamental task in the field of robotics and computer vision. There is considerable work in the field of deep camera relocalization, which directly estimates p…

cs.RO20202 cited

3-D Motion Capture of an Unmodified Drone with Single-chip Millimeter Wave Radar

Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang +2

Accurate motion capture of aerial robots in 3-D is a key enabler for autonomous operation in indoor environments such as warehouses or factories, as well as driving forward researc…

cs.RO2020

milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion

Chris Xiaoxuan Lu, Muhamad Risqi U. Saputra, Peijun Zhao +6

Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmente…

cs.RO20204 cited

Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference

Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu +3

Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reli…

cs.RO2018

Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning

Linhai Xie, Sen Wang, Stefano Rosa +2

Deep Reinforcement Learning (DRL) has been applied successfully to many robotic applications. However, the large number of trials needed for training is a key issue. Most of existi…

cs.RO2018

Learning with Stochastic Guidance for Navigation

Linhai Xie, Yishu Miao, Sen Wang +5

Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of hig…