You'd never guess this lock is smart by looking at it, and that's my favorite part. No bulk or screen; looks like a regular lock from both sides of the door. Impressive design with the battery hidden inside the lock bolt. App is beautiful and easy to use. Best with accessories that need to be purchased separately.

Quantum navigation could solve the military's GPS jamming problem The rise of GPS vulnerability is putting more resilient, atom-based navigational tools on the map. The Royal Navy partnered with Infleqtion to test a quantum clock on the uncrewed submarine XV Excalibur. In late September, a Spanish military plane carrying the country's defense minister to a base in Lithuania was reportedly the subject of a kind of attack --not by a rocket or anti-aircraft rounds, but by radio transmissions that jammed its GPS system. The flight landed safely, but it was one of thousands that have been affected by a far-reaching Russian campaign of GPS interference since the 2022 invasion of Ukraine. The growing inconvenience to air traffic and risk of a real disaster have highlighted the vulnerability of GPS and focused attention on more secure ways for planes to navigate the gauntlet of jamming and spoofing, the term for tricking a GPS receiver into thinking it's somewhere else. US military contractors are rolling out new GPS satellites that use stronger, cleverer signals, and engineers are working on providing better navigation information based on other sources, like cellular transmissions and visual data.

Abstract-- Human video demonstrations provide abundant training data for learning robot policies, but video alone cannot capture the rich contact signals critical for mastering manipulation. We introduce OSMO, an open-source wearable tactile glove designed for human-to-robot skill transfer . The glove features 12 three-axis tactile sensors across the fingertips and palm and is designed to be compatible with state-of-the-art hand-tracking methods for in-the-wild data collection. We demonstrate that a robot policy trained exclusively on human demonstrations collected with OSMO, without any real robot data, is capable of executing a challenging contact-rich manipulation task. On a real-world wiping task requiring sustained contact pressure, our tactile-aware policy achieves a 72% success rate, outperforming vision-only baselines by eliminating contact-related failure modes. We release complete hardware designs, firmware, and assembly instructions to support community adoption. Tactile sensing enables humans to excel at manipulation by providing real-time feedback about contact forces that vision alone cannot capture. Consider trying to dice a carrot from video alone; one cannot observe the nuanced force control that makes the task successful. Many different applied forces can result in nearly identical visual appearances, leaving critical information about force control invisible to vision.

We propose a tightly-coupled LiDAR/Polarization Vision/Inertial/Magnetometer/Optical Flow Odometry via Smoothing and Mapping (LPVIMO-SAM) framework, which integrates LiDAR, polarization vision, inertial measurement unit, magnetometer, and optical flow in a tightly-coupled fusion. This framework enables high-precision and highly robust real-time state estimation and map construction in challenging environments, such as LiDAR-degraded, low-texture regions, and feature-scarce areas. The LPVIMO-SAM comprises two subsystems: a Polarized Vision-Inertial System and a LiDAR/Inertial/Magnetometer/Optical Flow System. The polarized vision enhances the robustness of the Visual/Inertial odometry in low-feature and low-texture scenarios by extracting the polarization information of the scene. The magnetometer acquires the heading angle, and the optical flow obtains the speed and height to reduce the accumulated error. A magnetometer heading prior factor, an optical flow speed observation factor, and a height observation factor are designed to eliminate the cumulative errors of the LiDAR/Inertial odometry through factor graph optimization. Meanwhile, the LPVIMO-SAM can maintain stable positioning even when one of the two subsystems fails, further expanding its applicability in LiDAR-degraded, low-texture, and low-feature environments. Code is available on https://github.com/junxiaofanchen/LPVIMO-SAM.

It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built - in sensors that offer a cost - effective and straightforward way to assess road quality. However, prog ress in this area has been slow due to the lack of high - quality, standardized datasets. This paper discusses a new dataset created by a mobile app that collects sensor data from devices like GPS, accelerometers, gyroscopes, magnetometers, gravity sensors, and orientation sensors. This dataset is one of the few that integrates Geographic Information System (GIS) data with weather information and video footage of road conditions, providing a comprehensive understanding of road issues with geographic context . The dataset allows for a clearer analysis of road conditions by compiling essential data, including vehicle speed, acceleration, rotation rates, and magnetic field intensity, along with the visual and spatial context provided by GIS, weather, and video dat a. Its goal is to provide funding for initiatives that enhance traffic management, infrastructure development, road safety, and urban planning . Additionally, the dataset will be publicly accessible to promote further research and innovation in smart transp ortation systems.

Abstract-- The manifold extended Kalman filter (Manifold EKF) has found extensive application for attitude determination. Magnetometers employed as sensors for such attitude determination are easily prone to disturbances by their sensitivity to calibration and external magnetic fields. The TRIAD (Tri-Axial Attitude Determination) algorithm is well-known as a sub-optimal attitude estimator . In this article, we incorporate this sub-optimal feature of the TRIAD in mitigating the influence of the magnetometer reading in the pitch and roll axis determination in the Manifold EKF algorithm. We substantiate our results with experiments. Accurate orientation estimation is critical for a wide range of applications, such as in Unmanned Aerial V ehicles (UA Vs), mobile devices and robotics. Numerous studies have been dedicated to improving sensor orientation estimation.

Australia's forthcoming Aukus nuclear-powered submarines have been called the'apex predator of the oceans'. Australia's forthcoming Aukus nuclear-powered submarines have been called the'apex predator of the oceans'. Quantum sensing, satellite tracking and AI are part of an accelerating arms race in detection that should prompt a re-evaluation of Australia's defence strategy Military history is littered with the corpses of apex predators. All once possessed unassailable power - then were undermined, in some cases wiped out, by the march of new technology. " Speed and stealth and firepower," the head of the Australian Submarine Agency, Jonathan Mead, told the Guardian two years ago of Australia's forthcoming fleet of nuclear submarines.

--The extended and unscented Kalman filter, and the particle filter provide a robust framework for fault-tolerant attitude estimation on spacecraft. This paper explores how each filter performs for a large satellite in a low earth orbit. Additionally, various techniques, built on these filters, for fault detection, isolation and recovery from erroneous sensor measurements, are analyzed. Key results from this analysis include filter performance for various fault modes. Communication satellites, satellites conducting scientific research, and reentry vehicles are all examples of spacecraft that need to predict and control their attitude.

This paper presents a new approach for jointly calibrating magnetometers and inertial measurement units, focusing on improving calibration accuracy and computational efficiency. The proposed method formulates the calibration problem as a maximum a posteriori estimation problem, treating both the calibration parameters and orientation trajectory of the sensors as unknowns. This formulation enables efficient optimization with closed-form derivatives. The method is compared against two state-of-the-art approaches in terms of computational complexity and estimation accuracy. Simulation results demonstrate that the proposed method achieves lower root mean square error in calibration parameters while maintaining competitive computational efficiency. Further validation through real-world experiments confirms the practical benefits of our approach: it effectively reduces position drift in a magnetic field-aided inertial navigation system by more than a factor of two on most datasets. Moreover, the proposed method calibrated 30 magnetometers in less than 2 minutes. The contributions include a new calibration method, an analysis of existing methods, and a comprehensive empirical evaluation. Datasets and algorithms are made publicly available to promote reproducible research.

Integrated Navigation (IN) techniques have emerged as a promising solution by combining multiple sensor measurements, such as those obtained from Inertial Measurement Units (IMU), Global Navigation Satellite Systems (GNSS), and vision-based sensors. IN approaches offer significant advantages, including robustness, improved accuracy, and the ability to overcome the limitations of individual sensors. Among the various mathematical tools employed in IN, quaternions have garnered considerable attention for estimating a vehicle's attitude (orientation). Quaternions provide an elegant and compact representation of orientation, avoiding the limitations of traditional Euler angles, such as singularities and ambiguity.