Distributed Traffic Signal Control for Maximum Network Throughput

We propose a distributed algorithm for controlling traffic signals. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner. Simulation results show that our algorithm significantly outperforms SCATS, an adaptive traffic signal control system that is being used in many cities

Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker

Tracking the object 6-DoF pose is crucial for various downstream robot tasks and real-world applications. In this paper, we investigate the real-world robot task of aerial vision guidance for aerial robotics manipulation, utilizing category-level 6-DoF pose tracking. Aerial conditions inevitably introduce special challenges, such as rapid viewpoint changes in pitch and roll and inter-frame differences. To support these challenges in task, we firstly introduce a robust category-level 6-DoF pose tracker (Robust6DoF). This tracker leverages shape and temporal prior knowledge to explore optimal inter-frame keypoint pairs, generated under a priori structural adaptive supervision in a coarse-to-fine manner. Notably, our Robust6DoF employs a Spatial-Temporal Augmentation module to deal with the problems of the inter-frame differences and intra-class shape variations through both temporal dynamic filtering and shape-similarity filtering. We further present a Pose-Aware Discrete Servo strategy (PAD-Servo), serving as a decoupling approach to implement the final aerial vision guidance task. It contains two servo action policies to better accommodate the structural properties of aerial robotics manipulation. Exhaustive experiments on four well-known public benchmarks demonstrate the superiority of our Robust6DoF. Real-world tests directly verify that our Robust6DoF along with PAD-Servo can be readily used in real-world aerial robotic applications

A passive repetitive controller for discrete-time finite-frequency positive-real systems

This work proposes and studies a new internal model for discrete-time passive or finite-frequency positive-real systems which can be used in repetitive control designs to track or to attenuate periodic signals. The main characteristic of the proposed internal model is its passivity. This property implies closed-loop stability when it is used with discrete-time passive plants, as well as the broader class of discrete-time finite-frequency positive real plants. This work discusses the internal model energy function and its frequency response. A design procedure for repetitive controllers based on the proposed internal model is also presented. Two numerical examples are included.Peer Reviewe

A new passive repetitive controller for discrete-time finite-frequency positive-real systems

This work proposes a new repetitive controller for discrete-time finite-frequency positive-real systems which are required to track periodic references or to attenuate periodic disturbances. The main characteristic of the proposed controller is its passivity. This fact implies closed-loop stable behavior when it is used with discrete-time passive plants, but additional conditions must be fulfilled when it is used with a discretetime finite-frequency positive-real plant. These conditions are analyzed and a design procedure is proposed.Peer Reviewe

NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis

ProSGNeRF: Progressive Dynamic Neural Scene Graph with Frequency Modulated Auto-Encoder in Urban Scenes

Implicit neural representation has demonstrated promising results in view synthesis for large and complex scenes. However, existing approaches either fail to capture the fast-moving objects or need to build the scene graph without camera ego-motions, leading to low-quality synthesized views of the scene. We aim to jointly solve the view synthesis problem of large-scale urban scenes and fast-moving vehicles, which is more practical and challenging. To this end, we first leverage a graph structure to learn the local scene representations of dynamic objects and the background. Then, we design a progressive scheme that dynamically allocates a new local scene graph trained with frames within a temporal window, allowing us to scale up the representation to an arbitrarily large scene. Besides, the training views of urban scenes are relatively sparse, which leads to a significant decline in reconstruction accuracy for dynamic objects. Therefore, we design a frequency auto-encoder network to encode the latent code and regularize the frequency range of objects, which can enhance the representation of dynamic objects and address the issue of sparse image inputs. Additionally, we employ lidar point projection to maintain geometry consistency in large-scale urban scenes. Experimental results demonstrate that our method achieves state-of-the-art view synthesis accuracy, object manipulation, and scene roaming ability. The code will be open-sourced upon paper acceptance

Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization

Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free mobile robot localization in repetitive featureless environments. The proposed system adopts a coarse-to-fine structure, which mainly consists of two parts: magnetic field-based matching and laser scan matching. Firstly, the interpolated magnetic field map is built and the initial pose of the mobile robot is partly determined by the k-Nearest Neighbors (k-NN) algorithm. Next, with the fusion of prior initial pose information, the robot is localized by laser scan matching more accurately and efficiently. In our experiment, the mobile robot was successfully localized in a featureless rectangular corridor with a success rate of 88% and an average correct localization time of 6.6 seconds

Study on the Legal Status of the Arctic Navigation Routes

王丹维,所在单位:厦门大学法学院。电子邮箱:[email protected]。［文摘］北极地区目前尚存的海洋划界争端主要是巴伦支海(挪威vs.俄罗斯)和波弗特海(加拿大vs.美国)。①除此以外,目前最热议的无疑是北极航道的法律地位问题。随着全球气候变暖,北极海冰加速融化,一些科学家乐观预测,在未来30年内北冰洋将出现夏季无冰年,使北冰洋“黄金水道”开通成为可能。本文尝试对北极航道的法律地位进行研究,在国内外学者的研究基础上,将 西北航道分为航线S和航线N,北方海航道分为极地航线、高纬度航线、中央航线和滨海航线,提出不能将北极航道的法律地位单一化,不同航线应具有不同的法律地位。［Abstract］The most striking Arctic maritime delimitation dispute,in addition to the surviving controversies of the Barents Sea(Norway vs.Russia) and the Beaufort Sea(Canada vs. the United States),is undoubtedly the legal status of the Arctic Navigation Routes (ANR).①In light of the accelerated melting of Arctic sea ice with global warming,some scientists have optimistically forecasted that ice-free summer might occur within the next thirty years,which would make it possible to open a golden waterway in the Arctic Ocean.Based on the research of scholars in China and abroad,this paper,by dividing the Northwest Passage (NWP) into Route S and Route N,and the Northern Sea Route (NSR) into the Polar Route,the High-latitude Route,the Central Route,and the Coastal Route,attempts to present the idea that the legal status of ANR is not one-folded,and that different routes have different legal status

PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment

Neural implicit scene representations have recently shown encouraging results in dense visual SLAM. However, existing methods produce low-quality scene reconstruction and low-accuracy localization performance when scaling up to large indoor scenes and long sequences. These limitations are mainly due to their single, global radiance field with finite capacity, which does not adapt to large scenarios. Their end-to-end pose networks are also not robust enough with the growth of cumulative errors in large scenes. To this end, we introduce PLGSLAM, a neural visual SLAM system capable of high-fidelity surface reconstruction and robust camera tracking in real-time. To handle large-scale indoor scenes, PLGSLAM proposes a progressive scene representation method which dynamically allocates new local scene representation trained with frames within a local sliding window. This allows us to scale up to larger indoor scenes and improves robustness (even under pose drifts). In local scene representation, PLGSLAM utilizes tri-planes for local high-frequency features with multi-layer perceptron (MLP) networks for the low-frequency feature, achieving smoothness and scene completion in unobserved areas. Moreover, we propose local-to-global bundle adjustment method with a global keyframe database to address the increased pose drifts on long sequences. Experimental results demonstrate that PLGSLAM achieves state-of-the-art scene reconstruction results and tracking performance across various datasets and scenarios (both in small and large-scale indoor environments).Comment: Accepted by CVPR 202