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.

Loop Closure Detection With Reweighting Net VLAD and Local Motion and Structure Consensus

Dear Editor,Loop closure detection(LCD)is an important module in simultaneous localization and mapping(SLAM).In this letter,we address the LCD task from the semantic aspect to the geometric one.To this end,a network termed as AttentionNetVLAD which can simultaneously extract global and local features is proposed.It leverages attentive selection for local features,coupling with reweighting the soft assignment in NetVLAD via the attention map for global features.Given a query image,candidate frames are first identified coarsely by retrieving similar global features in the database via hierarchical navigable small world(HNSW).As global features mainly summarize the semantic information of images and lead to compact representation,information about spatial arrangement of visual elements is lost.

Long-term care for aged ethnic minority people in Yunnan,China:Understanding the situation

Chinese governmental sectors have been investing lots of money in setting up new institutions for long-term care(LTC)in rural China to respond to the predicted increasing and urgent needs of the growing number of aging people.However,very few have identified that many ethnic minority groups have been enjoying their traditional home-based LTC systems,which are cost-effective and have been working really well for generations.This article reports on a study on LTC needs and provisions for the Dai,one of the ethnic minority groups in rural areas of southwest China,in Yunnan(Province).A randomized sample was drawn from 12 villages in Ruili,a county 752 km from Kunming,the capital city of Yunnan.Totally,187 elderly persons were interviewed intensively.Activities of daily living were measured as the core indicator for understanding LTC.The research revealed that there is a significant disparity between the needs of the villagers and provisions from the different levels of government.There is an urgent need to reexamine the allocation of public resources for the aged.Traditional culture and practices of caring for the elderly,and practices in LTC of different ethnic groups,must be carefully considered.