Abstract
Lifelong mapping presents unique challenges to household robots which operate in the same environment over long durations. One is the growth of redundant information in the map as it evolves over time, which can easily overwhelm the limited computation resources of a household robot. Another is the possibility of mapping errors. An error in robot pose estimate, which if not corrected fast enough, will result in incorrect occupancy and semantic representation, rendering the map unusable. Finally, for a lifelong mapping system where the map is updated continuously, avoiding these errors altogether is infeasible. In this paper, we present a comprehensive overview of novel strategies for eliminating redundant information from the map and preventing and correcting mapping errors. We also present a detailed evaluation of these novel strategies on 10,000 robots running in indoor environments across different geographic locations of the world to demonstrate map stability and accuracy over time.
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