59 citations · 147 across the 18 of their papers we have counts for
19 papers
ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via Graph Optimization
Zongyang Chen, Xianfei Pan, Changhao Chen
Accurately and reliably positioning pedestrians in satellite-denied conditions remains a significant challenge. Pedestrian dead reckoning (PDR) is commonly employed to estimate ped…
Deep Learning for Inertial Positioning: A Survey
Changhao Chen, Xianfei Pan
Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based…
Arithmetic structure of the exceptional set of projections
Changhao Chen, Zhengyan Miao
We study the arithmetic structure of the exceptional set of projections. For any bounded subset , let We…
EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention
Zheming Tu, Changhao Chen, Xianfei Pan +3
Accurate and robust localization is a fundamental need for mobile agents. Visual-inertial odometry (VIO) algorithms exploit the information from camera and inertial sensors to esti…
The boundaries of 2+1D fermionic topological orders
Chang-Han Chen, Xiao-Gang Wen
D bosonic topological orders can be characterized by the matrices that encode the statistics of topological excitations. In particular, the matrices can be used to…
Metric theory of Weyl sums
Changhao Chen, Bryce Kerr, James Maynard +1
We prove that there exist positive constants and such that for any integer the set of satisfying $$ cN^{1/2}\le \left|\sum^N_{n=1}\exp\le…
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…
DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Chris Xiaoxuan Lu +7
Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fa…
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Chris Xiaoxuan Lu, Xuan Kan, Bowen Du +5
Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, fa…
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
Changhao Chen, Chris Xiaoxuan Lu, Bing Wang +2
Dynamical models estimate and predict the temporal evolution of physical systems. State Space Models (SSMs) in particular represent the system dynamics with many desirable properti…
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Changhao Chen, Stefano Rosa, Yishu Miao +4
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input…
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…
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu +3
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. Howe…
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…
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
Changhao Chen, Xiaoxuan Lu, Andrew Markham +1
Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly…