An SDN Oriented Loop Detection Mechanism Based on TTL Statistics

The rise of the network has injected new impetus into the development of traditional networks.Due to the complexity of the network itself and its network programmability,there is a risk of routing loops occurring in the SDN network.This paper proposes a loop detection mechanism.According to the Time To Live(TTL)value of the loop packet,there is approximately periodicity in the same loop.We use sFlow to count the number of packets corresponding to each TTL value of a switch in the loop over a period of time,and perform discrete Fourier transform on the obtained finite-length sequence to observe its frequency domain performance and determine whether there are periodic features.By doing so,it is determined whether there is a routing loop,and the purpose of passively detecting the routing loop is achieved.Compared to existing algorithms,it has advantages in real-time,scalability and false positive rate.The experimental results show that the routing loop detection algorithm based on TTL statistics in this paper still maintains high judgment accuracy under the scenarios of lower stream sampling rate and smaller detection period.

Loop Closure Detection via Locality Preserving Matching With Global Consensus

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D space.In this work,a novel appearance-based LCD system is proposed.Specifically,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task.It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance.To dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive structures.Meanwhile,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds.The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets.Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks.We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.

Visual SLAM Based on Object Detection Network:A Review

Visual simultaneous localization and mapping(SLAM)is crucial in robotics and autonomous driving.However,traditional visual SLAM faces challenges in dynamic environments.To address this issue,researchers have proposed semantic SLAM,which combines object detection,semantic segmentation,instance segmentation,and visual SLAM.Despite the growing body of literature on semantic SLAM,there is currently a lack of comprehensive research on the integration of object detection and visual SLAM.Therefore,this study aims to gather information from multiple databases and review relevant literature using specific keywords.It focuses on visual SLAM based on object detection,covering different aspects.Firstly,it discusses the current research status and challenges in this field,highlighting methods for incorporating semantic information from object detection networks into mileage measurement,closed-loop detection,and map construction.It also compares the characteristics and performance of various visual SLAM object detection algorithms.Lastly,it provides an outlook on future research directions and emerging trends in visual SLAM.Research has shown that visual SLAM based on object detection has significant improvements compared to traditional SLAM in dynamic point removal,data association,point cloud segmentation,and other technologies.It can improve the robustness and accuracy of the entire SLAM system and can run in real time.With the continuous optimization of algorithms and the improvement of hardware level,object visual SLAM has great potential for development.

Unified control and detection framework and its applications: a review, some new results, and future perspectives

Initiated three decades ago,integrated design of controllers and fault detectors has continuously attracted research attention.The recent development of the unified control and detection framework with an observer-based residual generator in its core gives a more general form of the previous works.Its applications to residual centred modelling of uncertain control systems,fault detection in feedback control systems with uncertainties,fault-tolerant control(FTC)as well as control performance degradation monitoring,detection and recovery are introduced.In conclusion,some future perspectives are proposed.

Probability Loop Closure Detection with Fisher Kernel Framework for Visual SLAM

A typical approach to describe an image in loop closure detection for visual SLAM is to extract a set of local patch descriptors and encode them into a co-occurrence vector.The most common patch encoding strategy is known as bag-of-visual-words(BoVW)representation,which consists of clustering the local descriptors into visual vocabulary.The distinctiveness of images is difficult to represent since most of them contain similar texture information,which may lead to false positive results.In this paper,the vocabulary is used as a whole by adopting the Fisher kernel(FK)framework.The new representation describes the image as the gradient vector of the likelihood function.The efficiently computed vectors can be compressed with a minimal loss of accuracy using product quantization and perform well in the task of loop closure detection.The proposed method achieves a higher recall rate with 100%precision in loop closure detection compared with state-of-the-art methods,and the detection on bidirectional loops is also enhanced.vSLAM systems may perceive the environment more efficiently by constructing a globally consistent map with the proposed loop closure detection method,which is potentially valuable for applications such as autonomous driving.