Andrew Markham

収録論文 44本 ・ フィジカルAI/ロボット学習

※arXiv著者名で収集。同姓同名の別人の論文が含まれる場合があります。

論文

• CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding
  2026/1/1
• COOPERA: Continual Open-Ended Human-Robot Assistance
  2025/10/1
• Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation
  2023/3/1
• Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
  2022/9/1
• When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation
  2022/6/1
• OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum
  2022/6/1
• RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
  2022/4/1
• Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV
  2022/3/1
• Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds
  2022/3/1
• SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds
  2022/1/1
• DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks
  2021/12/1
• Deep Odometry Systems on Edge with EKF-LoRa Backend for Real-Time Positioning in Adverse Environment
  2021/12/1
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
  2021/7/1
• SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
  2021/4/1
• Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
  2021/4/1
• RadarLoc: Learning to Relocalize in FMCW Radar
  2021/3/1
• 3-D Motion Capture of an Unmodified Drone with Single-chip Millimeter Wave Radar
  2020/11/1
• SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
  2020/11/1
• Demo Abstract: Indoor Positioning System in Visually-Degraded Environments with Millimetre-Wave Radar and Inertial Sensors
  2020/10/1
• Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
  2020/9/1
• A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
  2020/6/1
• milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion
  2020/6/1
• PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
  2020/3/1
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference
  2020/1/1
• Learning Selective Sensor Fusion for States Estimation
  2019/12/1
• DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
  2019/10/1
• DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
  2019/9/1
• Milli-RIO: Ego-Motion Estimation with Low-Cost Millimetre-Wave Radar
  2019/9/1
• DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
  2019/8/1
• Distilling Knowledge From a Deep Pose Regressor Network
  2019/8/1
• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
  2019/6/1
• Selective Sensor Fusion for Neural Visual-Inertial Odometry
  2019/3/1
• Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning
  2019/3/1
• Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
  2018/12/1
• Learning with Stochastic Guidance for Navigation
  2018/11/1
• Transferring Physical Motion Between Domains for Neural Inertial Tracking
  2018/10/1
• OxIOD: The Dataset for Deep Inertial Odometry
  2018/9/1
• Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
  2018/8/1
• Defo-Net: Learning Body Deformation using Generative Adversarial Networks
  2018/4/1
• IONet: Learning to Cure the Curse of Drift in Inertial Odometry
  2018/2/1
• Dense 3D Object Reconstruction from a Single Depth View
  2018/2/1
• 3D Object Reconstruction from a Single Depth View with Adversarial Learning
  2017/8/1
• Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
  2017/6/1
• Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
  2016/12/1