The device that was supposed to revolutionize was both'too ambitious and not ambitious enough.' More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Segway HT inventor, founder, Chairman and CEO Dean Kamen (center) demonstrates his New Personal Transporter on December 18, 2001 to Delphi Automotive Systems' President, Chairman, and CEO J.T. Battenberg (right) at Delphi's headquarters in Troy, Michigan. On the left is J. Douglas Field, Segway Company's Chief Engineer. Battenberg said the Segway HT is going to simplify personal travel in places that are impractical for automobiles.

An Indonesian farmer has gone viral for using a drone as a mode of transportation. Video shows his journey above ground in Java as he safely makes it to his destination.

Best Power Banks Best Smart Rings Routers vs. Modems Choose the Right Laptop Smart Sprinklers Deals Delivered The company is mapping Alexandria and, soon, Arlington--right across from the power center of Washington, DC. Self-driving cars aren't yet permitted to operate in Virginia. But Alphabet-owned Waymo began transporting its cars to the state last week, a Waymo representative told Virginia officials, to map Arlington and Alexandria, in the northern part of the state. For most autonomous vehicle companies, mapping, or the creation of sensor-aided and ultra-precise digital representations of streets and the features around them, is the first step required to launch a local robotaxi service. Drivers will operate the mapping vehicles for now, Waymo says.

This work presents a data-driven approach for the indoor localization of an observer on a 2D topological map of the environment. State-of-the-art techniques may yield accurate estimates only when they are tailor-made for a specific data modality like camera-based system that prevents their applicability to broader domains. Here, we establish a modality-agnostic framework (called OT-Isomap) and formulate the localization problem in the context of parametric manifold learning while leveraging optimal transportation. This framework allows jointly learning a lowdimensional embedding as well as correspondences with a topological map. We examine the generalizability of the proposed algorithm by applying it to data from diverse modalities such as image sequences and radio frequency signals. The experimental results demonstrate decimeter-level accuracy for localization using different sensory inputs.

Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates.

As an example, for an image taken on the ground, two text options24 are: {Several vehicles providing ground transportation are shown in25 the photo: streetcar, tour bus, classic car, and family cars.}

Here, we establish a modality-agnostic framework (calledOT-Isomap) and formulate the localization problem in the context of parametric manifold learning while leveraging optimal transportation.

Pre-trained vision-language models (VLMs) have shown impressive results in various visual classification tasks.However, we often fail to fully unleash their potential when adapting them for new concept understanding due to limited information on new classes.To address this limitation, we introduce a novel adaptation framework, AWT (Augment, Weight, then Transport). AWT comprises three key components: augmenting inputs with diverse visual perspectives and enriched class descriptions through image transformations and language models; dynamically weighting inputs based on the prediction entropy; and employing optimal transport to mine semantic correlations in the vision-language space.AWT can be seamlessly integrated into various VLMs, enhancing their zero-shot capabilities without additional training and facilitating few-shot learning through an integrated multimodal adapter module.We verify AWT in multiple challenging scenarios, including zero-shot and few-shot image classification, zero-shot video action recognition, and out-of-distribution generalization. AWT consistently outperforms the state-of-the-art methods in each setting. In addition, our extensive studies further demonstrate AWT's effectiveness and adaptability across different VLMs, architectures, and scales.

Cooperative suspended aerial transportation is highly susceptible to multi-source disturbances such as aerodynamic effects and thrust uncertainties. To achieve precise load manipulation, existing methods often rely on extra sensors to measure cable directions or the payload's pose, which increases the system cost and complexity. A fundamental question remains: is the payload's pose observable under multi-source disturbances using only the drones' odometry information? To answer this question, this work focuses on the two-drone-bar system and proves that the whole system is observable when only two or fewer types of lumped disturbances exist by using the observability rank criterion. To the best of our knowledge, we are the first to present such a conclusion and this result paves the way for more cost-effective and robust systems by minimizing their sensor suites. Next, to validate this analysis, we consider the situation where the disturbances are only exerted on the drones, and develop a composite disturbance filtering scheme. A disturbance observer-based error-state extended Kalman filter is designed for both state and disturbance estimation, which renders improved estimation performance for the whole system evolving on the manifold $(\mathbb{R}^3)^2\times(TS^2)^3$. Our simulation and experimental tests have validated that it is possible to fully estimate the state and disturbance of the system with only odometry information of the drones.