We present a novel framework for open-set Simultaneous Localization and Mapping (SLAM) in unstructured environments that uses segmentation to create a map of objects and geometric relationships between objects for localization. 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 to generate an object-based map and 2) a frame alignment pipeline that uses the geometric consistency of objects to efficiently localize within maps taken in a variety of conditions. This approach is shown to be more robust to changes in lighting and appearance than traditional feature-based SLAM systems or global descriptor methods. This is established by evaluating SOS-SLAM on the Batvik seasonal dataset which includes drone flights collected over a coastal plot of southern Finland during different seasons and lighting conditions. Across flights during varying environmental conditions, our approach achieves higher recall than benchmark methods with precision of 1.0. SOS-SLAM localizes within a reference map up to 14x faster than other feature based approaches and has a map size less than 0.4% the size of the most compact other maps. When considering localization performance from varying viewpoints, our approach outperforms all benchmarks from the same viewpoint and most benchmarks from different viewpoints. SOS-SLAM is a promising new approach for SLAM in unstructured environments that is robust to changes in lighting and appearance and is more computationally efficient than other approaches. We release our code and datasets: https://acl.mit.edu/SOS-SLAM/.

翻译：我们提出了一种新颖的非结构化环境开放集同时定位与建图（SLAM）框架，该框架利用分割技术创建物体地图，并通过物体间的几何关系实现定位。我们的系统包含：1）前端建图流水线，采用零样本分割模型从图像中提取物体掩膜，并跨帧跟踪以生成基于物体的地图；2）帧对齐流水线，利用物体的几何一致性，在不同条件下采集的地图中实现高效定位。实验表明，与传统基于特征的SLAM系统或全局描述子方法相比，该方法对光照和外观变化具有更强的鲁棒性。通过在Batvik季节性数据集（涵盖芬兰南部沿海地块不同季节及光照条件下采集的无人机飞行数据）上的评估，验证了这一结论。在多变环境条件下的飞行测试中，我们的方法在精确度为1.0时实现了比基准方法更高的召回率。SOS-SLAM在参考地图中的定位速度比基于特征的方法快达14倍，且地图规模小于其他最紧凑地图的0.4%。在视角变化的定位性能测试中，我们的方法在同视角下超越所有基准方法，并在不同视角下优于大部分基准方法。SOS-SLAM为光照与外观变化具有鲁棒性且计算效率更优的非结构化环境SLAM提供了一种有前景的新方案。我们已公开代码和数据集：https://acl.mit.edu/SOS-SLAM/。