59 citations · 97 across the 11 of their papers we have counts for
11 papers
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao +1
Human motion sensing plays a crucial role in smart systems for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predomina…
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals
Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang +2
mmWave FMCW radar has attracted huge amount of research interest for human-centered applications in recent years, such as human gesture/activity recognition. Most existing pipeline…
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Bing Wang, Changhao Chen, Zhaopeng Cui +8
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D loc…
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…
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…
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…
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…
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar
Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao +5
This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility…
AtLoc: Attention Guided Camera Localization
Bing Wang, Changhao Chen, Chris Xiaoxuan Lu +3
Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To…
DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
Wei Wang, Muhamad Risqi U. Saputra, Peijun Zhao +5
Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have br…
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…