LoopNet: A Multitasking Few-Shot Learning Approach for Loop Closure in Large Scale SLAM
This work addresses real-time SLAM challenges for robotics and autonomous systems, but it is incremental as it builds on existing ResNet and few-shot learning methods.
The paper tackles the loop closure detection accuracy and real-time computation constraints in SLAM systems by proposing LoopNet, a multitasking few-shot learning approach that uses DISK descriptors, achieving better performance under varying conditions and introducing a new benchmarking dataset called LoopDB.
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.