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Found 77 papers, 28 papers with code
Disturbance Observer-Parameterized Control Barrier Function with Adaptive Safety Bounds
This letter presents a nonlinear disturbance observer-parameterized control barrier function (DOp-CBF) designed for a robust safety control system under external disturbances.
An Efficient Scene Coordinate Encoding and Relocalization Method
Scene Coordinate Regression (SCR) is a visual localization technique that utilizes deep neural networks (DNN) to directly regress 2D-3D correspondences for camera pose estimation.
SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment
Lastly, we introduce a probabilistic feature association scheme that considers the entire probability density of assignments, which can manage uncertainties in measurement and initial pose estimation.
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation
While SFUDA is well-developed in visual tasks, its application to Time-Series SFUDA (TS-SFUDA) remains limited due to the challenge of transferring crucial temporal dependencies across domains.
A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
Remaining Useful Life (RUL) prediction is a critical aspect of Prognostics and Health Management (PHM), aimed at predicting the future state of a system to enable timely maintenance and prevent unexpected failures.
ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges
While UWB-based methods can achieve high localization accuracy in small-scale areas, their accuracy and reliability are significantly challenged in large-scale environments.
A Novel Edge Laplacian-based Approach for Adaptive Formation Control of Uncertain Multi-agent Systems with Unified Relative Error Performance
For most existing prescribed performance formation control methods, performance requirements are not directly imposed on the relative states between agents but on the consensus error, which lacks a clear physical interpretation of their solution.
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain
There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using RF sensors and fingerprinting-based methods that employ machine learning models trained on crowd-sourced user data gathered from IoT devices.
Salient Sparse Visual Odometry With Pose-Only Supervision
Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs.
Diffusion Model is a Good Pose Estimator from 3D RF-Vision
However, the mmWave radar has a limited resolution with severe noise, leading to inaccurate and inconsistent human pose estimation.
MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception
Perception plays a crucial role in various robot applications.
Reliable Spatial-Temporal Voxels For Multi-Modal Test-Time Adaptation
Motivated by the fact that reliable predictions should be consistent with their spatial-temporal correspondences, Latte aggregates consecutive frames in a slide window manner and constructs Spatial-Temopral (ST) voxels to capture temporally local prediction consistency for each modality.
PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing
The proposed method consists of a spatial smoothing module and a pose adjustment module, which combines the benefits of local consistency and global accuracy.
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data
To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools, yet their effectiveness is restricted by the quality of graph construction from MTS data.
T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognition
We find that the tiny model after network augmentation is much easier for a teacher to distill.
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition
Benefiting from our unsupervised learning procedure, the network requires only a small amount of annotated data for finetuning and can adapt to the new environment with better performance.
MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats
In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset.
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration
Subsequently, a generative adversarial network is trained to restore the corrupted regions by leveraging the semantic context learned from surrounding uncorrupted regions.
Distributed Semi-global Output Feedback Formation Maneuver Control of High-order Multi-agent Systems
Compared with existing relative-measurement-based formation maneuver control, the advantages of the proposed method are that it is output (relative output) feedback based and shows how to realize different types of formation shape.
Angle-Displacement Rigidity Theory with Application to Distributed Network Localization
This paper investigates the localization problem of a network in 2-D and 3-D spaces given the positions of anchor nodes in a global frame and inter-node relative measurements in local coordinate frames.
3-D Distributed Localization with Mixed Local Relative Measurements
Each node holds a local coordinate frame without a common orientation and can only measure one type of information (relative position, distance, relative bearing, angle, or ratio-of-distance measurements) about its neighboring nodes in its local coordinate frame.
Distributed Localization in Dynamic Networks via Complex Laplacian
Different from most existing distributed localization approaches in static networks where the agents in a network are static, this paper addresses the distributed localization problem in dynamic networks where the positions of the agents are time-varying.
Distributed Formation Maneuver Control Using Complex Laplacian
The third advantage is that more formation shapes can be realized by only tuning the positions of the leaders.
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both the local and global sensor levels.
TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition
Through the IoT-language contrastive learning, we derive a unified semantic feature space that aligns multi-modal features with language embeddings, so that the IoT data corresponds to specific words that describe the IoT data.
The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks
Dynamic quantized mechanisms are designed for inter-follower communication and leader-follower communication.
Integrated Relative-Measurement-Based Network Localization and Formation Maneuver Control (Extended Version)
This paper studies the problem of integrated distributed network localization and formation maneuver control.
AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi
The results demonstrate the effectiveness and robustness of AdaPose in eliminating domain shift, thereby facilitating the widespread application of WiFi-based pose estimation in smart cities.
MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation
In this work, we propose Multi-modal Prior Aided (MoPA) domain adaptation to improve the performance of rare objects.
Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?
These provide the first benchmark and important insights for the future development of 4D imaging radar-based online 3D MOT algorithms.
Graph-Aware Contrasting for Multivariate Time-Series Classification
As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data.
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT.
Quantized distributed Nash equilibrium seeking under DoS attacks: A quantized consensus based approach
To solve the quantizer saturation problem caused by DoS attacks, the quantization mechanism is equipped to have zooming-in and holding capabilities, in which the holding capability is consistent with the results in quantized consensus under DoS.
Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data
This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization.
Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Condition
Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to $\mathcal{O}(\log(T))$, and the network cumulative constraint violation bound is reduced to $\mathcal{O}(\sqrt{\log(T)T})$ and $\mathcal{O}(\log(T))$ without and with Slater's condition, respectively.
Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?
Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain.
MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing
Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks.
Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation
In this paper, we explore Multi-Modal Continual Test-Time Adaptation (MM-CTTA) as a new extension of CTTA for 3D semantic segmentation.
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction of large-scale datasets and deep learning-based representations.
AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization
The AirFi is a novel domain generalization framework that learns the critical part of CSI regardless of different environments and generalizes the model to unseen scenarios, which does not require collecting any data for adaptation to the new environment.
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.
MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation
Avatar refers to a representative of a physical user in the virtual world that can engage in different activities and interact with other objects in metaverse.
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models.
Quantized Consensus under Data-Rate Constraints and DoS Attacks: A Zooming-In and Holding Approach
This paper is concerned with the quantized consensus problem for uncertain nonlinear multi-agent systems under data-rate constraints and Denial-of-Service (DoS) attacks.
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
WiFi sensing has been evolving rapidly in recent years.
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain.
A Survey on Distributed Online Optimization and Game
And the local cost function of each agent is often time-varying in dynamic and even adversarial environments.
Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms
Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades.
AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning
The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance.
EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression
WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access.
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition
The results validate that our method works well on wireless human activity recognition and person identification systems.
Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization
We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution in expectation with an $\mathcal{O}(\frac{1}{T}+\frac{1}{\sqrt{T}})$ convergence rate, where $T$ is the total number of iterations.
Self-Supervised Video Representation Learning by Video Incoherence Detection
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning.
Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
A novel algorithm is first proposed and it achieves an $\mathcal{O}(T^{\max\{c, 1-c\}})$ bound for static regret and an $\mathcal{O}(T^{(1-c)/2})$ bound for cumulative constraint violation, where $c\in(0, 1)$ is a user-defined trade-off parameter, and thus has improved performance compared with existing results.
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk
Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention.
Self-Supervised Learning
Self-supervised Scene Flow Estimation
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence
Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks.
Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization
This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions.
Practical Output Consensus of Nonlinear Heterogeneous Multi-Agent Systems with Limited Data Rate
This paper investigates the consensus problem for nonlinear heterogeneous multi-agent systems with limited communication data rate.
Vision Based Autonomous UAV Plane Estimation And Following for Building Inspection
Unmanned Aerial Vehicle (UAV) has already demonstrated its potential in many civilian applications, and the fa\c{c}ade inspection is among the most promising ones.
Pose Estimation
Robotics
Systems and Control
Systems and Control
Robust Output Regulation and Reinforcement Learning-based Output Tracking Design for Unknown Linear Discrete-Time Systems
To remove such a requirement, an off-policy reinforcement learning algorithm is proposed using only the measured output data along the trajectories of the system and the reference output.
Dynamical Systems
VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach
However, some issues such as estimation drift and feature-dependence still remain inherent limitations.
Robotics Systems and Control Systems and Control
Online Visual Place Recognition via Saliency Re-identification
As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving.
Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training
The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures.
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network.
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM).
Fast Loop Closure Detection via Binary Content
Our method is compared with the state-of-the-art loop closure detection methods and the results show that it outperforms the traditional methods at both recall rate and speed.
Coordinate-free Circumnavigation of a Moving Target via a PD-like Controller
This paper proposes a coordinate-free controller for a nonholonomic vehicle to circumnavigate a fully-actuated moving target by using range-only measurements.
Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader
When the faster evader is allowed to move freely without any constraint, the main issues are how to form an encirclement to trap the evader into the capture domain, how to balance between forming an encirclement and approaching the faster evader, and what conditions make the capture possible.
Systems and Control Systems and Control
Kervolutional Neural Networks
Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks.
Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints
Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation.
Model-free Approach for Sensor Network Localization with Noisy Distance Measurement
In addition, different from the traditional filter-based localization methods which need kinetic model for localization, our proposed method is model-free and converts the localization problem to graph optimization problem.
Graph Optimization Approach to Range-based Localization
In this paper, we propose a general graph optimization based framework for localization, which can accommodate different types of measurements with varying measurement time intervals.
Robotics
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
Robust velocity and position estimation is crucial for autonomous robot navigation.
Non-iterative SLAM for Warehouse Robots Using Ground Textures
We present a novel visual SLAM method for the warehouse robot with a single downward-facing camera using ground textures.
Robotics
Ultra-Wideband Aided Fast Localization and Mapping System
This paper proposes an ultra-wideband (UWB) aided localization and mapping system that leverages on inertial sensor and depth camera.
Robotics
Kernel Cross-Correlator
Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking.
On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
This paper is concerned about sparse, continuous frequency estimation in line spectral estimation, and focused on developing gridless sparse methods which overcome grid mismatches and correspond to limiting scenarios of existing grid-based approaches, e. g., $\ell_1$ optimization and SPICE, with an infinitely dense grid.
