在DPV-SLAM中使用AnyLoc视觉位置识别实现回环闭合
Loop Closure using AnyLoc Visual Place Recognition in DPV-SLAM
AI总结：
本文提出在DPV-SLAM中使用AnyLoc改进回环闭合性能，通过深度特征提升鲁棒性并自适应调整阈值，实验表明其在精度和稳定性上优于传统方法。
摘要：
回环闭合对于保持视觉SLAM的精度和一致性至关重要。我们提出了一种方法来提高DPV-SLAM中的回环闭合性能。我们的方法将AnyLoc，一种基于学习的视觉位置识别技术，作为经典Bag of Visual Words（BoVW）回环检测方法的替代方案。与BoVW不同，后者依赖于手工设计的特征，AnyLoc利用深度特征表示，使在多样化的视角和光照条件下都能实现更稳健的图像检索。此外，我们提出了一种自适应机制，能够根据环境条件动态调整相似性阈值，从而无需手动调参。在室内和室外数据集上的实验表明，我们的方法在回环闭合的准确性和鲁棒性方面显著优于原始的DPV-SLAM。所提出的方法为现代SLAM系统提供了一种实用且可扩展的解决方案，以提高回环闭合性能。
英文摘要：
Loop closure is crucial for maintaining the accuracy and consistency of visual SLAM. We propose a method to improve loop closure performance in DPV-SLAM. Our approach integrates AnyLoc, a learning-based visual place recognition technique, as a replacement for the classical Bag of Visual Words (BoVW) loop detection method. In contrast to BoVW, which relies on handcrafted features, AnyLoc utilizes deep feature representations, enabling more robust image retrieval across diverse viewpoints and lighting conditions. Furthermore, we propose an adaptive mechanism that dynamically adjusts similarity threshold based on environmental conditions, removing the need for manual tuning. Experiments on both indoor and outdoor datasets demonstrate that our method significantly outperforms the original DPV-SLAM in terms of loop closure accuracy and robustness. The proposed method offers a practical and scalable solution for enhancing loop closure performance in modern SLAM systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
