Abstract
Despite recent advances in semantic Simultaneous Localization and Mapping (SLAM) for terrestrial and aerial applications, underwater semantic SLAM remains an open and largely unaddressed research problem due to the unique sensing modalities and the object classes found underwater. This paper presents an object-based semantic SLAM method for underwater environments that can identify, localize, classify, and map a wide variety of marine objects without a priori knowledge of the object classes present in the scene. The method performs unsupervised object segmentation and object-level feature aggregation, and then uses opti-acoustic sensor fusion for object localization. Probabilistic data association is used to determine observation to landmark correspondences. Given such correspondences, the method then jointly optimizes landmark and vehicle position estimates. Indoor and outdoor underwater datasets with a wide variety of objects and challenging acoustic and lighting conditions are collected for evaluation and made publicly available. Quantitative and qualitative results show the proposed method achieves reduced trajectory error compared to baseline methods, and is able to obtain comparable map accuracy to a baseline closed-set method that requires hand-labeled data of all objects in the scene.
Abstract (translated)
尽管在陆地和空中应用中，最近取得了同步定位与映射（SLAM）的进展，但水下语义SLAM仍然是一个开放且主要尚未解决的研究问题，原因是水下中发现了独特的感测模式和物体类别。本文提出了一种基于对象的语义SLAM方法，可以无先验知识识别、定位、分类和映射广泛的海洋物体。该方法进行无监督的物体分割和物体级特征聚合，然后使用可选声传感器融合进行对象定位。概率数据关联用于确定观测到地标对应关系。根据这些对应关系，该方法然后共同优化地标和车辆位置估计。室内和室外的水下数据集，包括各种物体和具有挑战性的声学和照明条件，用于评估并公开提供。定量和定性结果表明，与基线方法相比，所提出的方法减少了轨迹误差，并且能够获得与需要手工标注场景中所有物体相同的地形准确性。
URL
https://arxiv.org/abs/2403.12837
