activity
20182023
most citedOxIOD: The Dataset for Deep Inertial Odometry

59 citations · 97 across the 11 of their papers we have counts for

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Showing cs.ROShow all

5 papers · 1 filter

cs.RO2020★ 2 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★ 10 cited

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.RO2020★ 5 cited

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

Wei Wang, Bing Wang, Peijun Zhao +5

In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as in…

cs.RO2020★ 4 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★ 59 cited

OxIOD: The Dataset for Deep Inertial Odometry

Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu +3

Advances in micro-electro-mechanical (MEMS) techniques enable inertial measurements units (IMUs) to be small, cheap, energy efficient, and widely used in smartphones, robots, and d…