SOS-Match：面向非结构化环境中开集鲁棒对应搜索与机器人定位的分割方法
SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments
AI总结：
针对非结构化环境的机器人定位问题，提出SOS-Match框架，结合零样本分割目标跟踪与几何一致性帧对齐，在Batvik数据集上实现光照/外观鲁棒性、视角不变性提升，定位速度快46倍且地图更小。
摘要：
我们提出SOS-Match，一种用于非结构化环境中检测和匹配目标的新型框架。该系统包含两部分：1) 前端建图流水线，使用零样本分割模型从图像中提取目标掩码并跨帧跟踪；2) 帧对齐流水线，利用目标关系的几何一致性在多种条件下高效定位。我们在Batvik季节性数据集上评估SOS-Match，该数据集包含芬兰南部沿海地块在不同季节和光照条件下采集的无人机飞行数据。结果表明，相比经典图像特征方法或全局描述符方法，我们的方法对光照和外观变化更鲁棒，且比基于学习的特征检测与描述方法具有更强的视角不变性。SOS-Match在参考地图内定位速度比其他特征方法快46倍，地图大小不足其他最紧凑地图的0.5%。SOS-Match是非结构化环境中地标检测和对应搜索的有前景新方法，对光照和外观变化鲁棒且计算效率更高，表明分割的几何排列是非结构化环境中宝贵的定位线索。我们在https://acl.mit.edu/SOS-Match/发布数据集。
英文摘要：
We present SOS-Match, a novel framework for detecting and matching objects in unstructured environments. Our system consists of 1) a front-end mapping pipeline using a zero-shot segmentation model to extract object masks from images and track them across frames and 2) a frame alignment pipeline that uses the geometric consistency of object relationships to efficiently localize across a variety of conditions. We evaluate SOS-Match on the Batvik seasonal dataset which includes drone flights collected over a coastal plot of southern Finland during different seasons and lighting conditions. Results show that our approach is more robust to changes in lighting and appearance than classical image feature-based approaches or global descriptor methods, and it provides more viewpoint invariance than learning-based feature detection and description approaches. SOS-Match localizes within a reference map up to 46x faster than other feature-based approaches and has a map size less than 0.5% the size of the most compact other maps. SOS-Match is a promising new approach for landmark detection and correspondence search in unstructured environments that is robust to changes in lighting and appearance and is more computationally efficient than other approaches, suggesting that the geometric arrangement of segments is a valuable localization cue in unstructured environments. We release our datasets at https://acl.mit.edu/SOS-Match/.
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。
