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: this https URL

一个新颖的开放式同步定位与地图构建（SLAM）框架，通过分割方法在无结构环境中创建对象地图和对象之间的几何关系以进行定位，该方法相对于传统基于特征和全局描述符的 SLAM 系统更具鲁棒性，在照明和外观变化方面表现更好，并且在定位性能方面超过了基准方法。