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Found 28 papers, 13 papers with code
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning.
Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings
In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks in 3D point clouds.
GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning
As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications.
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars
Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc.
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
WiFi sensing has been evolving rapidly in recent years.
STUN: Self-Teaching Uncertainty Estimation for Place Recognition
Then, supervised by the pretrained teacher net, a student net with an additional variance branch is trained to finetune the embedding priors and estimate the uncertainty sample by sample.
Self-Supervised Scene Flow Estimation with 4-D Automotive Radar
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy.
Multiagent Model-based Credit Assignment for Continuous Control
By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control.
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather
As a result, it learns to generate minimal image perturbations that are still capable of perplexing the network.
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals
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.
Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment.
Probabilistic Deep Learning
Simultaneous Localization and Mapping
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds.
Deep Inertial Odometry with Accurate IMU Preintegration
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors.
Demo Abstract: Indoor Positioning System in Visually-Degraded Environments with Millimetre-Wave Radar and Inertial Sensors
In this demonstration, we present a real-time indoor positioning system which fuses millimetre-wave (mmWave) radar and IMU data via deep sensor fusion.
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
Deep learning based localization and mapping has recently attracted significant attention.
Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference
Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots.
Learning Selective Sensor Fusion for States Estimation
By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e. g. locations and orientations.
Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning
Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms.
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar
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.
DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
The hallucination network is taught to predict fake visual features from thermal images by using Huber loss.
Milli-RIO: Ego-Motion Estimation with Millimetre-Wave Radar and Inertial Measurement Unit Sensor
With the fast-growing demand of location-based services in various indoor environments, robust indoor ego-motion estimation has attracted significant interest in the last decades.
AtLoc: Attention Guided Camera Localization
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.
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
In addition we show how DynaNet can indicate failures through investigation of properties such as the rate of innovation (Kalman Gain).
mID: Tracking and Identifying People with Millimeter Wave Radar
The key to offering personalised services in smart spaces is knowing where a particular person is with a high degree of accuracy.
Selective Sensor Fusion for Neural Visual-Inertial Odometry
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 sensory data.
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent.
OxIOD: The Dataset for Deep Inertial Odometry
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.
