Multi-Session Ground Texture SLAM in Low-Dynamic Environments
For robotics researchers needing lifelong SLAM in environments where ground texture is the only feature, this work provides a method and dataset for handling low-dynamic changes.
This work addresses multi-session SLAM in low-dynamic environments using ground texture, where surface wear or weather changes occur. The Kullback-Leibler Divergence method improved trajectory estimation accuracy, and a new multi-session dataset with ground changes and high-accuracy poses is introduced.
The simultaneous localization and mapping community has introduced a growing number of systems adapted for multi-session operations where the operational environment features low-dynamic changes that impact mapping, such as surface wear, weather phenomena, or seasonal change. These systems allow for lifelong operations by a robot within these environments. There is also growing interest in operations in environments where the unique ground texture is the only mapping feature available for use. These ground texture systems are not yet targeted for multi-session low-dynamic-change environments though. This work explores the impact of three different techniques on trajectory estimation accuracy in these multi-session low-dynamic ground texture environments. Of the three, the use of Kullback-Leibler Divergence, as a similarity score and a bias influencing loop closure confidence, is found to have the most success. We show an analysis of all three methods and a deeper exploration of the impact of Kullback-Leibler Divergence. We also introduce a dataset for use by the robotics community that contains multi-session images where the ground changes between sessions and also high-accuracy pose information for use in evaluation.