262 citations · 1.3k across the 36 of their papers we have counts for
11 papers · 1 filter
Deep Odometry Systems on Edge with EKF-LoRa Backend for Real-Time Positioning in Adverse Environment
Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu +2
Ubiquitous positioning for pedestrian in adverse environment has served a long standing challenge. Despite dramatic progress made by Deep Learning, multi-sensor deep odometry syste…
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…
Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
Linhai Xie, Sen Wang, Stefano Rosa +2
Deep Reinforcement Learning (DRL) has been applied successfully to many robotic applications. However, the large number of trials needed for training is a key issue. Most of existi…
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…
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…
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Zhihua Wang, Stefano Rosa, Linhai Xie +4
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (Defo-Net), able to pre…
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…
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
Linhai Xie, Sen Wang, Andrew Markham +1
Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding c…
Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Ronald Clark, Sen Wang, Hongkai Wen +2
Localization is a key requirement for mobile robot autonomy and human-robot interaction. Vision-based localization is accurate and flexible, however, it incurs a high computational…