Search Results for author: Peijun Zhao

Found 11 papers, 5 papers with code

milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing

no code implementations29 Jun 2023 Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu

In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks.

Decision Making Gesture Recognition +4

CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals

1 code implementation7 Nov 2021 Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang, Niki Trigoni, Andrew Markham

To avoid the drawbacks of conventional DFT pre-processing, we propose a learnable pre-processing module, named CubeLearn, to directly extract features from raw radar signal and build an end-to-end deep neural network for mmWave FMCW radar motion recognition applications.

Activity Recognition

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

2 code implementations5 Mar 2020 Wei Wang, Bing Wang, Peijun Zhao, Changhao Chen, Ronald Clark, Bo Yang, Andrew Markham, Niki Trigoni

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 input, without requiring a pre-built map.


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

no code implementations13 Jan 2020 Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu, Wei Wang, Andrew Markham, Niki Trigoni

Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots.

See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar

1 code implementation1 Nov 2019 Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, John A. Stankovic, Niki Trigoni, Andrew Markham

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 environments to assist in emergency response.

DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network

no code implementations13 Oct 2019 Wei Wang, Muhamad Risqi U. Saputra, Peijun Zhao, Pedro Gusmao, Bo Yang, Changhao Chen, Andrew Markham, Niki Trigoni

There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images.

Translation Visual Odometry

AtLoc: Attention Guided Camera Localization

1 code implementation8 Sep 2019 Bing Wang, Changhao Chen, Chris Xiaoxuan Lu, Peijun Zhao, Niki Trigoni, Andrew Markham

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.

Camera Localization Visual Localization

OxIOD: The Dataset for Deep Inertial Odometry

no code implementations20 Sep 2018 Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu, Wei Wang, Andrew Markham, Niki Trigoni

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 drones.

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