Cooperative Localization under Limited Connectivity

We report two decentralized multi-agent cooperative localization algorithms in which, to reduce the communication cost, inter-agent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown inter-agent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown inter-agent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic~experiment.Comment: 9 pages, 5 figure

Investigación del Mercado y Marketing de vehículos de nueva energía en China

El mercado de vehículos de nueva energía en China es un mercado en rápido desarrollo, impulsado por política gubernamentales de apoyo y una creciente conciencia sobre la protección del medio ambiente. Analizamos la situación macro y microeconómica del mercado para obtener una visión general. La tecnología y el rendimiento de los vehículos de nueva energía están mejorando gradualmente, mientras que los precios son cada vez más razonables, lo que hace que los consumidores estén más dispuestos a comprarlos. Además, a través del análisis DAFO de Tesla y BYD, podemos entender mejor sus productos y su situación de comercialización, y obtener inspiración sobre la viabilidad del mercado chino.Grado en Comerci

STRUKTUR KOMUNITAS ECHINODERMATA DI PERAIRAN PANTAI GAPANG, DESA IBOIH, KECAMATAN SUKAKARYA, SABANG

Banda Ace

Inhibition of SPRY2 expression protects sevoflurane-induced nerve injury via ERK signaling pathway

Purpose: To investigate the effect of Sprouty2 (SPRY2) on sevoflurane (SEV) induced nerve injury in rats and its potential signaling pathway. Methods: Male Sprague-Dawley rats were divided into sham and SEV groups containing six rats per group. Neurological injury assessment and H & E staining were performed to evaluate the degree of nerve injury in the rats, while quantitative polymerase chain reaction (qPCR) and immunoblot assays were performed to confirm the expression levels of SPRY2 in hippocampus tissues. Morris water maze tests were performed to determine the degree of cognitive deficit in rats. TUNEL and immunoblot assays were performed to evaluate the effects of SPRY2 on the apoptosis of hippocampus tissues. Results: The SPRY2 expression was elevated in sevoflurane-induced hippocampus injury (p < 0.001). Ablation of SPRY2 inhibited sevoflurane-induced hippocampal neuron apoptosis (p < 0.001). In addition, depletion of SPRY2 promoted hippocampal neuron activity and decreased apoptosis (p < 0.001). Knockdown of SPRY2 promoted ERK signaling pathway, thereby protecting against sevoflurane-induced nerve injury and cognitive deficit in the rats (p < 0.001). Conclusion: Sevoflurane induces cognitive dysfunction and upregulates SPRY2 expression in brain tissues in rats. The SPRY2 knockdown improves SEV-induced neural injuries and cognitive deficits, inhibits hippocampal neuron apoptosis, and enhances its activity. Meanwhile, SPRY2 depletion protects SEV-induced nerve injury via the ERK pathway. Thus, Sprouty2 could serve as a promising drug target for the treatment of SEV-induced cognitive dysfunctions

SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar

The 4D Millimeter wave (mmWave) radar is a promising technology for vehicle sensing due to its cost-effectiveness and operability in adverse weather conditions. However, the adoption of this technology has been hindered by sparsity and noise issues in radar point cloud data. This paper introduces spatial multi-representation fusion (SMURF), a novel approach to 3D object detection using a single 4D imaging radar. SMURF leverages multiple representations of radar detection points, including pillarization and density features of a multi-dimensional Gaussian mixture distribution through kernel density estimation (KDE). KDE effectively mitigates measurement inaccuracy caused by limited angular resolution and multi-path propagation of radar signals. Additionally, KDE helps alleviate point cloud sparsity by capturing density features. Experimental evaluations on View-of-Delft (VoD) and TJ4DRadSet datasets demonstrate the effectiveness and generalization ability of SMURF, outperforming recently proposed 4D imaging radar-based single-representation models. Moreover, while using 4D imaging radar only, SMURF still achieves comparable performance to the state-of-the-art 4D imaging radar and camera fusion-based method, with an increase of 1.22% in the mean average precision on bird's-eye view of TJ4DRadSet dataset and 1.32% in the 3D mean average precision on the entire annotated area of VoD dataset. Our proposed method demonstrates impressive inference time and addresses the challenges of real-time detection, with the inference time no more than 0.05 seconds for most scans on both datasets. This research highlights the benefits of 4D mmWave radar and is a strong benchmark for subsequent works regarding 3D object detection with 4D imaging radar

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, most of the camera-based perception methods transform the extracted image perspective view features into the bird's-eye view geometrically via "depth-based splatting" proposed in Lift-Splat-Shoot (LSS), and some researchers exploit other modals such as LiDARs or ordinary automotive radars for enhancement. Recently, a few works have applied the "sampling" strategy for image view transformation, showing that it outperforms "splatting" even without image depth prediction. However, the potential of "sampling" is not fully unleashed. In this paper, we investigate the "sampling" view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. In the proposed model, LXL, predicted image depth distribution maps and radar 3D occupancy grids are utilized to aid image view transformation, called "radar occupancy-assisted depth-based sampling". Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms existing 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings