ObVi-SLAM: Long-Term Object-Visual SLAM
ObVi-SLAM: Long-Term Object-Visual SLAM
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DOI：
10.1109/lra.2024.3363534
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发表时间：
2023-09
影响因子：
5.2
通讯作者：
Amanda Adkins;Taijing Chen;Joydeep Biswas
中科院分区：
文献类型：
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作者：
Amanda Adkins;Taijing Chen;Joydeep Biswas
负责长时间任务的机器人必须能够在几何、视点和外观变化中一致地、可扩展地定位。现有的视觉SLAM方法依赖于低级特征描述符，这些描述符对此类环境变化不鲁棒，并导致地图尺寸较大，在长期部署中扩展能力较差。相比之下，对象检测对环境变化是鲁棒的，并且导致更紧凑的表示，但是大多数基于对象的SLAM系统针对具有接近对象的短期室内部署。在这封信中，我们介绍了ObVi-SLAM，以克服这些挑战，利用这两种方法中最好的。ObVi-SLAM使用低级别视觉功能进行高质量的短期视觉测距;为了确保全局的长期一致性，ObVi-SLAM构建了持久对象的不确定性感知长期地图，并在每次部署后更新。通过评估ObVi-SLAM的数据，从16个部署会话跨越不同的天气和照明条件，我们经验表明，ObVi-SLAM生成准确的定位估计一致的长期尺度，尽管不同的外观条件。
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 letter, 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.