Map Management for Efficient Long-Term Visual Localization in Outdoor Environments
This addresses the challenge of efficient visual localization for autonomous vehicles or robots in outdoor environments, though it appears incremental as it builds on existing map summarization and update techniques.
The paper tackles the problem of managing large visual data for long-term outdoor localization in resource-constrained multi-vehicle systems, proposing a map management process that enhances appearance coverage and boosts landmark selection, enabling precise localization over long time spans in a tractable and scalable way.
We present a complete map management process for a visual localization system designed for multi-vehicle long- term operations in resource constrained outdoor environments. Outdoor visual localization generates large amounts of data that need to be incorporated into a lifelong visual map in order to allow localization at all times and under all appearance conditions. Processing these large quantities of data is non- trivial, as it is subject to limited computational and storage capabilities both on the vehicle and on the mapping backend. We address this problem with a two-fold map update paradigm capable of, either, adding new visual cues to the map, or updating co-observation statistics. The former, in combination with offline map summarization techniques, allows enhancing the appearance coverage of the lifelong map while keeping the map size limited. On the other hand, the latter is able to significantly boost the appearance-based landmark selection for efficient online localization without incurring any additional computational or storage burden. Our evaluation in challenging outdoor conditions shows that our proposed map management process allows building and maintaining maps for precise visual localization over long time spans in a tractable and scalable fashion.