ModaLink：统一模态以实现高效的图像到点云位置识别
ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
AI总结：
提出一种快速轻量的跨模态位置识别框架ModaLink，通过视场转换模块和非负分解编码器统一图像与点云模态，在KITTI和HAOMO数据集上实现实时且最先进的检索性能。
摘要：
位置识别是机器人和自动驾驶汽车在预建地图中实现自身定位和闭环检测的重要任务。虽然基于单模态传感器的方法已表现出令人满意的性能，但从点云数据库中检索图像的跨模态位置识别仍然是一个具有挑战性的问题。当前的跨模态方法利用深度估计将图像转换为3D点以进行模态转换，这通常计算量大且需要昂贵的标注数据进行深度监督。在本研究中，我们引入了一个快速且轻量级的框架，将图像和点云编码为具有位置区分性的描述符。我们提出了一个有效的视场（FoV）转换模块，将点云转换为与图像类似的模态。该模块消除了深度估计的需要，并帮助后续模块实现实时性能。我们进一步设计了一个基于非负分解的编码器，以提取点云和图像之间相互一致的语义特征。该编码器生成更具区分性的全局描述符以用于检索。在KITTI数据集上的实验结果表明，我们提出的方法在实时运行下实现了最先进的性能。在覆盖17公里轨迹的HAOMO数据集上的额外评估进一步展示了其实际泛化能力。我们已在以下网址开源了我们方法的实现：https://github.com/haomo-ai/ModaLink.git。
英文摘要：
Place recognition is an important task for robots and autonomous cars to localize themselves and close loops in pre-built maps. While single-modal sensor-based methods have shown satisfactory performance, cross-modal place recognition that retrieving images from a point-cloud database remains a challenging problem. Current cross-modal methods transform images into 3D points using depth estimation for modality conversion, which are usually computationally intensive and need expensive labeled data for depth supervision. In this work, we introduce a fast and lightweight framework to encode images and point clouds into place-distinctive descriptors. We propose an effective Field of View (FoV) transformation module to convert point clouds into an analogous modality as images. This module eliminates the necessity for depth estimation and helps subsequent modules achieve real-time performance. We further design a non-negative factorization-based encoder to extract mutually consistent semantic features between point clouds and images. This encoder yields more distinctive global descriptors for retrieval. Experimental results on the KITTI dataset show that our proposed methods achieve state-of-the-art performance while running in real time. Additional evaluation on the HAOMO dataset covering a 17 km trajectory further shows the practical generalization capabilities. We have released the implementation of our methods as open source at: https://github.com/haomo-ai/ModaLink.git.
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。
