Robots responsible for tasks over long time scales must be able to localize consistently and scalably amid geometric, viewpoint, and appearance changes. Existing visual SLAM approaches rely on low-level feature descriptors that are not robust to such environmental changes and result in large map sizes that scale poorly over long-term deployments. In contrast, object detections are robust to environmental variations and lead to more compact representations, but most object-based SLAM systems target short-term indoor deployments with close objects. In this paper, we introduce ObVi-SLAM to overcome these challenges by leveraging the best of both approaches. ObVi-SLAM uses low-level visual features for high-quality short-term visual odometry; and to ensure global, long-term consistency, ObVi-SLAM builds an uncertainty-aware long-term map of persistent objects and updates it after every deployment. By evaluating ObVi-SLAM on data from 16 deployment sessions spanning different weather and lighting conditions, we empirically show that ObVi-SLAM generates accurate localization estimates consistent over long-time scales in spite of varying appearance conditions.

翻译：在长时间尺度上执行任务的机器人必须能够在几何、视角和外观变化的情况下保持一致且可扩展的定位能力。现有的视觉SLAM方法依赖于对环境变化不够鲁棒的低级特征描述子，且会产生在长期部署中可扩展性差的大规模地图。相比之下，物体检测对环境变化具有鲁棒性，并能生成更紧凑的表示，但大多数基于物体的SLAM系统针对近距离物体的短期室内部署。本文提出ObVi-SLAM，通过融合两种方法的优势来克服这些挑战。ObVi-SLAM使用低级视觉特征进行高质量的短期视觉里程计；同时，为确保全局长期一致性，ObVi-SLAM构建了一个考虑不确定性的持久物体长期地图，并在每次部署后进行更新。通过使用跨越不同天气和光照条件的16个部署会话数据对ObVi-SLAM进行评估，我们实验证明，尽管外观条件不断变化，ObVi-SLAM仍能生成在长时间尺度上保持一致的准确定位估计。