Erasing bad memories: Agent-side summarization for long-term mapping

Precisely estimating the pose of an agent in a global reference frame is a crucial goal that unlocks a multitude of robotic applications, including autonomous navigation and collaboration. In order to achieve this, current state-of-the-art localization approaches collect data provided by one or more agents and create a single, consistent localization map, maintained over time. However, with the introduction of lengthier sorties and the growing size of the environments, data transfers between the backend server where the global map is stored and the agents are becoming prohibitively large. While some existing methods partially address this issue by building compact summary maps, the data transfer from the agents to the backend can still easily become unmanageable. In this paper, we propose a method that is designed to reduce the amount of data that needs to be transferred from the agent to the backend, functioning in large-scale, multi-session mapping scenarios. Our approach is based upon a landmark selection method that exploits information coming from multiple, possibly weak and correlated, landmark utility predictors; fused using learned feature coefficients. Such a selection yields a drastic reduction in data transfer while maintaining localization performance and the ability to efficiently summarize environments over time. We evaluate our approach on a data set that was autonomously collected in a dynamic indoor environment over a period of several months.