Adverse imaging conditions such as fog and dust present substantial challenges to optical camera and Light Detection and Ranging (LiDAR), which in turn highlights millimeter-wave radar as a promising alternative for reliable mapping. However, reflection losses and severe multipath interference often render millimeter-wave radar pointcloud sparse and noisy, thereby limiting their applicability in 3D imaging. In this letter, we present a novel conditional Generative Adversarial Network (cGAN) for reconstructing dense, high-resolution 3D maps from sparse millimeter-wave radar pointcloud. The Non-Coherent Accumulation (NCA) and Synthetic Aperture Accumulation (SAA) strategies are designed to address the issues of raw pointcloud sparsity and low angular resolution, respectively. Then this framework employs the PointNet++-based generator-discriminator architecture, leveraging LiDAR-labeled NCA and SAA pointcloud as conditional input for pointcloud-to-pointcloud (Pcd2Pcd) translation. The proposed method is evaluated on the publicly available RadarEyes dataset, where experimental results demonstrate that it outperforms existing approaches, achieving higher resolution, improved accuracy, and superior generalization capability.

Visual SLAM algorithms have been enhanced through the exploration of Gaussian Splatting representations, particularly in generating high-fidelity dense maps. While existing methods perform reliably in static environments, they often encounter camera tracking drift and fuzzy mapping when dealing with the disturbances caused by moving objects. This paper presents DyPho-SLAM, a real-time, resource-efficient visual SLAM system designed to address the challenges of localization and photorealistic mapping in environments with dynamic objects. Specifically, the proposed system integrates prior image information to generate refined masks, effectively minimizing noise from mask misjudgment. Additionally, to enhance constraints for optimization after removing dynamic obstacles, we devise adaptive feature extraction strategies significantly improving the system’s resilience. Experiments conducted on publicly dynamic RGB-D datasets demonstrate that the proposed system achieves state-of-the-art performance in camera pose estimation and dense map reconstruction, while operating in real-time in dynamic scenes.

We present KD-RIEKF, a novel state estimation framework that incorporates kinodynamic constraints into the Right-Invariant Extended Kalman Filter (RIEKF). Our framework integrates generalized momentum-based contact estimation, centroidal dynamics, and a noise-adaptive module, improving state estimation accuracy by probabilistically adjusting propagation noise to account for contact uncertainty and sensor noise. A key innovation is the expansion of the ground reaction force (GRF) into a state variable. By using GRF-based acceleration as a measurement, our method significantly reduces estimation errors in position, velocity, and orientation. The integration of contact-force-driven adaptive noise effectively boosts the stability of estimation, especially when the system is undergoing turning, acceleration, or deceleration processes. We validated our algorithm in simulation on highly uneven terrain, showing significant enhancements in z-axis position estimation compared to RIEKF. Further experiments on the Unitree Go2 robot across different speeds demonstrated that even in high-speed scenarios over 200 meters, our method reduced position estimation relative error (RE) by 47% and orientation estimation by 42%, confirming its robustness and accuracy under dynamic locomotion.

Enhanced sensor technology and advanced control algorithms have expanded the use of autonomous Unmanned Aerial Vehicles (UAVs) in critical sectors such as power inspection. However, the limitations in positioning accuracy and onboard computational power constrain the robustness and versatility of UAV motion planning algorithms in outdoor environments. Therefore, we enhanced the existing generalized UAV obstacle avoidance pathfinding methods for application in electric power inspection, ensuring effective performance despite limited GPS signal quality and computational power. Initially, we delineate impassable areas by marking the non-collision space beneath obstacles at a specific height based on actual requirements. Next, environmental factors are integrated into the assessment of local target points, mitigating risks of UAVs encountering obstacles during trajectory planning. Finally, by discretizing the output B-spline trajectory with node fitness, we tightly couple the UAV's real-time position with the initiation of trajectory replanning. Experimental results confirm the method's robustness and efficiency.

This paper presents an efficient and safe method to avoid static and dynamic obstacles based on LiDAR. First, point cloud is used to generate a real-time local grid map for obstacle detection. Then, obstacles are clustered by DBSCAN algorithm and enclosed with minimum bounding ellipses (MBEs). In addition, data association is conducted to match each MBE with the obstacle in the current frame. Considering MBE as an observation, Kalman filter (KF) is used to estimate and predict the motion state of the obstacle. In this way, the trajectory of each obstacle in the forward time domain can be parameterized as a set of ellipses. Due to the uncertainty of the MBE, the semi-major and semi-minor axes of the parameterized ellipse are extended to ensure safety. We extend the traditional Control Barrier Function (CBF) and propose Dynamic Control Barrier Function (D-CBF). We combine D-CBF with Model Predictive Control (MPC) to implement safety-critical dynamic obstacle avoidance. Experiments in simulated and real scenarios are conducted to verify the effectiveness of our algorithm. The source code is released for the reference of the community11Code: https://github.com/jianzhuozhuTHU/MPC-D-CBF..

Industrial control systems (ICSs) are widely used in various industries. These systems have become prime targets for cyber and physical attacks. The attacks, which have an impact on the physical processes of an ICS, often lead to system misbehaviors through data contamination. Recent research has shown that network-based detection methods cannot monitor the physical level activities well enough to mitigate hybrid cyber attacks and cannot entirely protect ICSs. To protect ICSs from such threats, we propose a Spatio-Temporal Autoencoder (STA) with a Dynamic Thresholding Mechanism. The STA learns the normal physical behaviors of the system by capturing deep spatio-temporal dependencies to form a unified representation of the system state. The unified representation is decoded to reconstruct the input features. Then, the dynamic threshold is used to detect and locate the anomalies. We validate the STA using data set from a real water treatment plant testbed, SWaT. Evaluation results indicate the superior performance of the STA compared with six state-of-the-art methods, achieving an average improvement of 5.8% in the F-Score.

Autonomous navigation of ground robots has been widely used in indoor structured 2D environments, but there are still many challenges in outdoor 3D unstructured environments, especially in rough, uneven terrains. This paper proposed a plane-fitting based uneven terrain navigation framework (PUTN) to solve this problem. The implementation of PUTN is divided into three steps. First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based algorithm called Plane Fitting RRT*(PF- RRT*) is proposed to obtain a sparse trajectory. Each sampling point corresponds to a custom traversability index and a fitted plane on the point cloud. These planes are connected in series to form a traversable “strip”. Second, Gaussian Process Regression is used to generate traversability of the dense trajectory interpolated from the sparse trajectory, and the sampling tree is used as the training set. Finally, local planning is performed using nonlinear model predictive control (NMPC). By adding the traversability index and uncertainty to the cost function, and adding obstacles generated by the real-time point cloud to the constraint function, a safe motion planning algorithm with smooth speed and strong robustness is available. Experiments in real scenarios are conducted to verify the effectiveness of the method. The source code is released for the reference of the community11Source code: https://github.com/jianzhuozhuTHU/putn..

A lightweight intelligent vehicle system was developed to realize autonomous driving, face recognition, face anti-spoofing, remote control, infrared obstacle avoidance and other functions to improve the security of contactless delivery. In this system, BCM2711 was used as kernel control chip, and it was equipped with deep network learning models such as LaneNet, ResNet and LSTM. It had been proved that this system could realize the above functions and achieve real-time effects, thus gaining great economic value and market space in contactless delivery service.

An improved NS traffic flow model was built in this paper to simulate two safety factors of vehicle-pedestrian avoidance and vehicle-vehicle avoidance under different weather conditions. Then the regulations of changes on lanes and vehicle speed under two-lane conditions were optimized as well as the improved NS model based on cellular automata. Results showed that the improved NS model can predict road conditions effectively, thereby improving the safety of roads.

In this paper, we propose a variant of the signature file, called bit-sliced bloom-filtered signature file (BBS), as the basis for implementing filter-and-refine strategies for mining frequent patterns. In the filtering step, the candidate patterns are obtained by scanning BBS instead of the database. The resultant candidate set contains a superset of the frequent patterns. In the refinement phase, each algorithm refines the candidate set to prune away the false drops. Based on this indexing structure, we study two filtering (single and dual filter) and two refinement (sequential scan and probe) mechanisms, thus giving rise to four different strategies. We conducted an extensive performance study to study the effectiveness of BBS, and compared the four proposed processing schemes with the traditional a priori algorithm and the recently proposed FP-tree scheme. Our results show that BBS, as a whole, outperforms the a priori strategy. Moreover, one of the schemes that is based on dual filter and probe refinement performs the best in all cases.

Web servers manage large numbe rof documents of widely variable sizes.Moreover, the access patterns on the documents may also c hange over time.While some documents are highly popular over a prolonged period of time, we expe c tnewly added documents to increase in popularity while demand for most older documents decreases.It is therefore important to design eective caching strategy at the web server.In this paper, we present our approach to the problem.Our main contribution lies in the design of a novel prefetching strategy, called RAP.RAP identi es a set of association rules from the Web server's access log.Unlike existing mining strategy, RAP's miner values recently added log records more than earlier log records.Based on the rules, RAP predicts and prefetches documents from users initial requests.We conducted extensive study to evaluate RAP.The results show that RAP signi cantly outperforms existing schemes.We also show that the mining and caching cost is relatively low.