用少样本学习提升大规模SLAM中回环检测精度与实时性
LoopNet: A Multitasking Few-Shot Learning Approach for Loop Closure in Large Scale SLAM
- 基于改进的ResNet架构，支持在线小样本微调
- 在动态视觉数据集上实现高精度回环识别，优于传统方法
- 专为嵌入式设备优化，适合移动机器人实时定位场景
SLAM中的回环检测面临两个核心挑战：准确识别已访问位置，以及在嵌入式硬件上满足实时计算要求。本文提出LoopNet，一种基于多任务ResNet架构的少样本学习方法，支持在动态视觉数据集上在线重训练，并针对嵌入式设备进行优化。该模型不仅能输出查询图像在数据库中的索引，还能评估预测置信度。通过融合DISK特征描述子，超越手工设计特征与传统深度学习方法，在不同环境条件下表现更优。代码与新基准数据集LoopDB已开源。
原文摘要 · Abstract (English)
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.
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