59 citations · 147 across the 18 of their papers we have counts for
7 papers · 1 filter
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…