ObVi-SLAM: Long-Term Object-Visual SLAM
This addresses the challenge of scalable and consistent localization for robots in long-term deployments, representing an incremental improvement by integrating object-based mapping into visual SLAM.
The paper tackles the problem of long-term robot localization amid environmental changes by introducing ObVi-SLAM, which combines visual features for short-term odometry with an object-based map for consistency, achieving accurate localization across 16 deployment sessions with varying conditions.
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.