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Field Reconstruction-Based Non-Rendezvous Calibration for Low-Cost Mobile Sensors

Field Reconstruction-Based Non-Rendezvous Calibration for Lo...

ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Luo, Ji Hu, Yiling Yu, Chengzhao Hong, Chaopeng Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Shenzhen Environm Thinking Sci & Technol ETST Co Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Ecol Remediat & Carbon Sequestra Inst Environm & Ecol Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China RISC V Int Open Source Lab Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

Low-cost air pollution sensors (LCS) deployed on urban vehicles (e.g., taxis, buses) have emerged as a cost-effective solution for fine-grained air pollution monitoring. However, these mobile LCSs suffer from measurement drifting in real-world scenarios, necessitating a post-deployment real-time calibration. Unfortunately, the limited availability of urban real reference stations (RRS) restricts the calibration opportunities for LCSs. This paper proposes a non-rendezvous method that addresses this challenge by establishing virtual reference stations (VRS), which offer additional calibration opportunities for LCSs. Through the air pollution field reconstruction, the readings of VRSs are inferred from RRSs' data. Furthermore, a confidence assessment mechanism is developed to quantify the uncertainty of established VRSs. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method, showcasing a 25% improvement over the advanced baseline.

关键词： Low-cost sensor post-deployment calibration field reconstruction

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TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms

TransformLoc: Transforming MAVs into Mobile Localization Inf...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Wang, Haoyang Xu, Jingao Zhao, Chenyu Lu, Zihong Cheng, Yuhan Chen, Xuecheng Zhang, Xiao-Ping Liu, Yunhao Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798350383515;9798350383508

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs' unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68% in localization performance, motivating up to 60% navigation success rate improvement.

关键词： Micro air vehicle (MAV)

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DeformNet: Latent Space Modeling and Dynamics Prediction for Deformable Object Manipulation

DeformNet: Latent Space Modeling and Dynamics Prediction for...

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IEEE international Conference on Robotics and Automation (ICRA)

作者： Li, Chenchang Ai, Zihao Wu, Tong Li, Xiaosa Ding, Wenbo Xu, Huazhe Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci IIIS Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Manipulating deformable objects is a ubiquitous task in household environments, demanding adequate representation and accurate dynamics prediction due to the objects' infinite degrees of freedom. This work proposes DeformNet, which utilizes latent space modeling with a learned 3D representation model to tackle these challenges effectively. The proposed representation model combines a PointNet encoder and a conditional neural radiance field (NeRF), facilitating a thorough acquisition of object deformations and variations in lighting conditions. To model the complex dynamics, we employ a recurrent state-space model (RSSM) that accurately predicts the transformation of the latent representation over time. Extensive simulation experiments with diverse objectives demonstrate the generalization capabilities of DeformNet for various deformable object manipulation tasks, even in the presence of previously unseen goals. Finally, we deploy DeformNet on an actual UR5 robotic arm to demonstrate its capability in real-world scenarios.

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MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT 24

MobiAir: Unleashing Sensor Mobility for City-scale and Fine-...

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22nd Annual international Conference on Mobile Systems, Applications and Services (MOBISYS)

作者： Liu, Yuxuan Wang, Haoyang Man, Fanhang Xu, Jingao Dang, Fan Liu, Yunhao Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400705816

Mobile air pollution sensing methods are developed to collect air quality data with higher spatial-temporal resolutions. However, existing methods cannot process the spatially mixed gas samples effectively due to the highly dynamic temporal and spatial fluctuations experienced by the sensor, leading to significant measurement deviations. We find an opportunity to tackle the problem by exploring the potential patterns from sensor measurements. In light of this, we propose MobiAir, a novel city-scale fine-grained air quality estimation system to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model to discern mixed gas concentrations. Second, we design a knowledge-informed training strategy leveraging massive unlabeled city-scale data to enhance the AirBERT performance. To ensure the practicality of MobiAir, we have invested significant efforts in implementing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered air quality data at a city-scale for more than 1200 hours. Experiments conducted on collected data show that MobiAir reduces sensing errors by 96.7% with only 44.9 ms latency, outperforming the SOTA baseline by 39.5%.

关键词： AQI Monitoring Mobile Crowd-Sensing Self-supervised Learning

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Poster: Fusing Event and Depth Sensing for Dynamic Objects Localization and Tracking 24

Poster: Fusing Event and Depth Sensing for Dynamic Objects L...

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25th international Workshop on Mobile Computing Systems and Applications (HOTMOBILE)

作者： Wang, Haoyang Luo, Xinyu Ruan, Ciyu Chen, Xuecheng Ding, Wenhua Liu, Yuxuan Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China South China Univ Technol Guangzhou Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400704970

Background and motivation. The localization and tracking of high-speed dynamic objects find applications across various domains. For example, tracking unmanned aerial vehicles (UAVs) serves a crucial role for regulatory authorities, enabling the swift identification of illicitly operated *** work. Presently, the widespread use of cameras and Radar is observed in the realm of locating and tracking moving objects [1]. An illustrative example is [2], which integrates an RGB camera with Radar for object tracking, yielding follow-up effects in AR applications. Despite their successes, these methods encounter notable latency when tracking high-speed dynamic objects. The latency often extends into the hundreds of milliseconds due to several factors: ① cameras necessitate tens of milliseconds for exposure and may be susceptible to motion blur; ② algorithms may require a sequence of frames for optimizing localization results. This extended latency becomes a critical limitation when dealing with objects moving at speeds surpassing 10m/s, as the system is left with inadequate time for a response. Consequently, current methods fall short of meeting our requirement for precise real-time tracking of dynamic *** Insight. Nowadays, event camera, characterized by its asynchronous and motion-activated nature, boasting microsecond level temporal resolution, is finding application in high-frequency monitoring. This trend serves as a catalyst for our exploration into the integration of these asynchronous measurements with advanced vision techniques, aiming to enhance the tracking for high-speed ***. Albeit inspiring, translating it into a practical system is non-trivial and encounters significant challenges:• C1: Event burst and depth obtain delay impair object localization. Event cameras demonstrate enhanced sensitivity to changes in the environment. Crucial events triggered by objects are often overshadowed by the abundance of events tri

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CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised Learning

CaliFormer: Leveraging Unlabeled Measurements to Calibrate S...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Wang, Haoyang Liu, Yuxuan Zhao, Chenyu He, Jiayou Ding, Wenbo Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China RISC V Int Open Source Lab Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

Accurate calibration of low-cost sensors is critical for improving their potential in environmental monitoring. State-of-the-art (SOTA) methods based on supervised learning commonly calibrate sensor measurements with ground truth from the immediate past or future. However, these techniques rely heavily on labeled data which is challenging to obtain in real-world scenarios. Thus, this paper introduces CaliFormer, a novel representation learning model using self-supervised learning to extract time- and spatial-invariant knowledge from unlabeled measurements. Moreover, we propose pre-training enhancements and model architecture modifications to help train CaliFormer. We then fine-tune the calibration model with the learned representations, which is supervised by limited labeled data. Finally, we comprehensively evaluate our calibration model with a dataset collected by low-cost sensors. Results show that our model outperforms other SOTA calibration methods significantly.

关键词： Air Pollution Sensor calibration Self-supervised learning

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Poster: Olfactory Sensing in Turbulent Airflow via Collaborative Robots 24

Poster: Olfactory Sensing in Turbulent Airflow via Collabora...

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25th international Workshop on Mobile Computing Systems and Applications (HOTMOBILE)

作者： Cheng, Yuhan Chen, Xuecheng Yang, Yixuan Wang, Haoyang Liu, Yuxuan Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400704970

Unforeseen gas leaks can swiftly create a highly flammable atmosphere, which will lead to risks of explosions, health hazards, as well as long-term environmental impacts, especially when the gas is toxic. Hence, localizing the gas source as soon as possible remains paramount to executing subsequent mitigation measures, such as shutting off valves and effectively containing or sealing the leaks. Assigning human operators to engage in these laborious activities poses a risk to their health and lives. Deploying autonomous mobile olfactory robots for gas source localization (GSL) presents a solution with great potential, as they can search the environment efficiently without risking human lives. We task gas leakage in a factory with intricate pipelines as an example as shown in fig. 1(a). Here, a fleet of robots equipped with gas concentration sensors [4] and wind sensors are dispatched to search the emission ***, existing approaches are not able to offer feasible GSL solutions for mobile olfaction-based robots in realistic environments [1]. Specifically, the patchy nature of gas plumes and the discontinuity of the concentration field confuses and traps the robots. As demonstrated by experimental data in fig. 1(b), plumes fragment into disjointed patches spatially. This intermittent characteristic introduces uncertainty in the source direction when robots devise paths using noisy and local sensory data, ultimately leading to their entrapment within these *** work aims to deploy and schedule a team of robots to conduct efficient GSL in realistic patchy gas environments. We develop GasHunter, a collaborative strategy for multiple robots by exchange-based team roles adaptation to balance the search efficiency and localization effectiveness of gas sources. Robots are assigned heterogeneous roles that represent different planning preferences. For example, some robots are responsible for inspecting spurious concentration maxima (exploiter), while othe

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Visual-Tactile Fusion for Transparent Object Grasping in Complex Backgrounds

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IEEE TRANSACTIONS ON ROBOTICS 2023年第5期39卷 3838-3856页

作者： Li, Shoujie Yu, Haixin Ding, Wenbo Liu, Houde Ye, Linqi Xia, Chongkun Wang, Xueqian Zhang, Xiao-Ping Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Berkeley Shenzhen Inst RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Shanghai Univ Inst Artificial Intelligence Collaborat Innovat Ctr Marine Artificial Intellige Shanghai 200444 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

The grasping of transparent objects is challenging but of significance to robots. In this article, a visual-tactile fusion framework for transparent object grasping in complex backgrounds is proposed, which synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. First, we propose a multiscene synthetic grasping dataset named SimTrans12 K together with a Gaussian-mask annotation method. Next, based on the TaTa gripper, we propose a grasping network named transparent object-grasping convolutional neural network for grasping position detection, which shows good performance in both synthetic and real scenes. Inspired by human grasping, a tactile calibration method and a visual-tactile fusion classification method are designed, which improve the grasping success rate by 36.7% compared with direct grasping and the classification accuracy by 39.1%. Furthermore, the tactile height sensing module and the tactile position exploration module are added to solve the problem of grasping transparent objects in irregular and visually undetectable scenes. The experimental results demonstrate the validity of the framework.

关键词： Complex backgrounds synthetic transparent object dataset tactile calibration transparent object grasping visual-tactile fusion

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TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification 21

TriboGait: A deep learning enabled triboelectric gait sensor...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Li, Jiarong Wang, Zihan Zhao, Zihao Jin, Yuchao Yin, Jihong Huang, Shao-Lun Wang, Jiyu Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst RISC V Int Open Source Lab Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9781450384612

Accurately and continuously measuring and collecting data on human gait is critical for human activity recognition and individual identification, enabling various applications in smart homes/buildings, including security authentication, personal healthcare, and intelligent automation. Many sensing technologies have been investigated by researchers recently, such as camera-based, laser-based, and mobile approaches, which have limitations in particular sensing situations, such as environments with fewer privacy concerns, line-of-sight, and the use of wearables, etc. On the other hand, the floor with the embedded sensor is stable and robust to different circumstances, enabling non-intrusive gait recognition and human identification. Therefore, a triboelectric nanogenerator (TENG)-based gait sensor system installed on the floor is proposed in this paper. Our approach has many advantages in comparison to the existing gait recognition systems, including low cost, simple fabrication, lightweight, and high durability. The TENG-based sensors can be simply embedded into a smart carpet to discern mechanical motions through electrical signals. Furthermore, a deep learning model, deep residual bidirectional long short-term memory network with dense layers (Residual Dense-BiLSTM), is proposed for multichannel floor-based gait recognition. By utilizing this model to analyze the electrical outputs, our system can accurately detect various human activities and distinguish different individuals' walking patterns, with a recognition rate over 98% and 97%, respectively. We conclude that the proposed deep learning enabled triboelectric gait sensor system has broad applications in security and healthcare.

关键词： floor sensor deep learning activity recognition human identification

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SATac: A Thermoluminescence Enabled Tactile Sensor for Concurrent Perception of Temperature, Pressure, and Shear

SATac: A Thermoluminescence Enabled Tactile Sensor for Concu...

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IEEE international Conference on Robotics and Automation (ICRA)

作者： Song, Ziwu Yu, Ran Zhang, Xuan Sou, Kit Wa Mu, Shilong Peng, Dengfeng Zhang, Xiao-Ping Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Shenzhen Univ Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

ISBN: (纸本)9798350384581;9798350384574

Most vision-based tactile sensors use elastomer deformation to infer tactile information, which can not sense some modalities, like temperature. As an important part of human tactile perception, temperature sensing can help robots better interact with the environment. In this work, we propose a novel multi-modal vision-based tactile sensor, SATac, which can simultaneously perceive information on temperature, pressure, and shear. SATac utilizes the thermoluminescence of strontium aluminate to sense a wide range of temperatures with exceptional resolution. Additionally, the pressure and shear can also be perceived by analyzing the Voronoi diagram. A series of experiments are conducted to verify the performance of our proposed sensor. We also discuss the possible application scenarios and demonstrate how SATac could benefit robot perception capabilities.

关键词： Shear flow

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