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1. KGNv2: Separating Scale and Pose Prediction for Keypoint-Based 6-DoF Grasp Synthesis on RGB-D Input

   https://doi.org/10.1109/IROS55552.2023.10342514  

   Chen, Yiye; Xu, Ruinian; Lin, Yunzhi; Chen, Hongyi; Vela, Patricio A (October 2023, IEEE)

   We propose an improved keypoint approach for 6-DoF grasp pose synthesis from RGB-D input. Keypoint-based grasp detection from image input demonstrated promising results in a previous study, where the visual information provided by color imagery compensates for noisy or imprecise depth measurements. However, it relies heavily on accurate keypoint prediction in image space. We devise a new grasp generation network that reduces the dependency on precise keypoint estimation. Given an RGB-D input, the network estimates both the grasp pose and the camera-grasp length scale. Re-design of the keypoint output space mitigates the impact of keypoint prediction noise on Perspective-n-Point (PnP) algorithm solutions. Experiments show that the proposed method outperforms the baseline by a large margin, validating its design. Though trained only on simple synthetic objects, our method demonstrates sim-to-real capacity through competitive results in real-world robot experiments. 

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2. SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization

   https://doi.org/10.3390/s24196327  

   Mottakin, Khairul; Davuluri, Kiran; Allison, Mark; Song, Zheng (October 2024, Sensors)

   Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches. 

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3. Robust Hybrid Kalman Filter for a Class of Nonlinear Systems

   https://doi.org/10.23919/ACC.2018.8431473  

   Malladi, Bharani P.; Sanfelice, Ricardo G.; Butcher, Eric A. (June 2018, American Control Conference)

   Motivated by real-world applications with intermittent sensor data, an extended Kalman filter is formulated as a hybrid system and constructive conditions on its parameters guaranteeing an asymptotic stability property are provided. The dynamical properties of the estimation error are first characterized infinitesimally so to yield bounds on the rate of convergence and overshoot that depend on the parameters. By recasting the problem as the stabilization of a compact set, robustness properties of the proposed algorithm in the presence of disturbances in the system dynamics as well as measurement noise in the output are established. The proposed strategy is applied to spacecraft relative motion control with position-only measurements. 

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4. Learning-Based State Estimation for Automated Lane-Changing

   https://doi.org/10.1109/ITSC58415.2024.10919550  

   Ha, Won Yong; Chakraborty, Sayan; Lin, Xiaoyi; Ozbay, Kaan; Jiang, Zhong-Ping (September 2024, IEEE)

   This paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often depend on comprehensive system state information, which may not always be accessible or accurate due to dynamic traffic environments and sensor limitations. Our methodology dynamically adapts to these uncertainties and sensor noise by iteratively refining its control policy based on real-time sensor data and reconstructed states. We implemented an experimental setup featuring a scaled vehicle equipped with GPS, IMUs, and cameras, all processed through an Nvidia Jetson AGX Xavier board. This approach is pivotal as it addresses the limitations of simulations, which often fail to capture the complexity of dynamic real-world conditions. The results from real-world experiments demonstrate that our learning-based control system achieves smoother and more consistent lane-changing behavior compared to traditional direct measurement approaches. This paper underscores the effectiveness of integrating Adaptive Dynamic Programming (ADP) with state estimation techniques, as demonstrated through small-scale experiments. These experiments are crucial as they provide a practical validation platform that simulates real-world complexities, representing a significant advancement in the control systems used for autonomous driving. 

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5. An Electrochemical Impedance Spectrum-Based State of Health Differential Indicator with Reduced Sensitivity to Measurement Errors for Lithium–Ion Batteries

   https://doi.org/10.3390/batteries10100368  

   Abu_Qahouq, Jaber (October 2024, Batteries Journal)

   As the use of electrochemical batteries, especially lithium–ion (Li-Ion) batteries, increases due to emerging applications and expanding markets, the accurate and fast estimation of their state of health (SOH) is becoming increasingly important. The accuracy of the SOH estimation is highly dependent on the correlation strength between the used indicator and SOH and the accuracy of the SOH indicator measurement. This paper presents a new differential indicator which has a strong and consistent correlation with the SOH of Li-Ion batteries, based on a new Electrochemical Impedance Spectrum (EIS) Phase–Magnitude relationship. It is shown in this paper that the EIS Phase–Magnitude relationship exhibits a phase-based differential impedance magnitude SOH indicator between a first-phase peak point and a last-phase valley point. Because of the differential nature of this SOH indicator and because the two impedance values are measured at a phase peak point and a valley phase point regardless of the phase absolute values, the effect of impedance measurement shift/offset (error) on SOH estimation is reduced. This supports the future development of more accurate and faster online and offline SOH estimation algorithms and systems that have a higher immunity to impedance measurement shift/offset (error). Furthermore, in this work, the EIS was measured for a lithium–ion battery that was down to a ~15% SOH, which was not only used to support the conclusions of this paper, but also helped in filling a gap in the literature for EIS data under deep/high degradation levels. 

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