LoopNet: A Multitasking Few-Shot Learning Approach for Loop Closure in Large Scale SLAM
One of the main challenges in the Simultaneous Localization and Mapping (SLAM) loop closure problem is the recognition of previously visited places. In this work, we tackle the two main problems of real-time SLAM systems: 1) loop closure detection accuracy and 2) real-time computation constraints on the embedded hardware. Our LoopNet method is based on a multitasking variant of the classical ResNet architecture, adapted for online retraining on a dynamic visual dataset and optimized for embedded devices. The online retraining is designed using a few-shot learning approach. The architecture provides both an index into the queried visual dataset, and a measurement of the prediction quality. Moreover, by leveraging DISK (DIStinctive Keypoints) descriptors, LoopNet surpasses the limitations of handcrafted features and traditional deep learning methods, offering better performance under varying conditions. Code is available at https://github.com/RovisLab/LoopNet. Additinally, we introduce a new loop closure benchmarking dataset, coined LoopDB, which is available at https://github.com/RovisLab/LoopDB.
Code (0)
등록된 구현이 없습니다.
Tasks
Few-Shot LearningSimilar Papers 제목 키워드 기반
LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters
Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmon…
Information RetrievalMusic Information RetrievalRetrievalRhythmA Loopback Network for Explainable Microvascular Invasion Classification
Microvascular invasion (MVI) is a critical factor for prognosis evaluation and cancer treatment. The current diagnosis of MVI relies on pathologists to manually find out cancerous cells from hundreds of blood vessels…
Binary ClassificationClassificationDiagnosticMedical Image Analysis+1PinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM
Loop closure is essential to reduce drift and build globally consistent maps in large-scale environments. However, reliable loop closure with only geometric information from, e.g., a LiDAR sensor, remains challenging due…
Pose EstimationPoint CloudsLoop Closure Detection with RGB-D Feature Pyramid Siamese Networks
In visual Simultaneous Localization And Mapping (SLAM), detecting loop closures has been an important but difficult task. Currently, most solutions are based on the bag-of-words approach. Yet the possibility of deep neur…
Loop Closure DetectionSimultaneous Localization and MappingAppearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
In appearance-based localization and mapping, loop closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. As the size of the internal map in…
Loop Closure Detection