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Many modern simultaneous localization and mapping (SLAM) techniques rely onsparse landmark-based maps due to their real-time performance. However, thesetechniques frequently assert that these landmarks are fixed in position overtime, known as the static-world assumption. This is rarely, if ever, the casein most real-world environments. Even worse, over long deployments, robots arebound to observe traditionally static landmarks change, for example when anautonomous vehicle encounters a construction zone. This work addresses thischallenge, accounting for changes in complex three-dimensional environmentswith the creation of a probabilistic filter that operates on the features thatgive rise to landmarks. To accomplish this, landmarks are clustered intocliques and a filter is developed to estimate their persistence jointly amongobservations of the landmarks in a clique. This filter uses estimatedspatial-temporal priors of geometric objects, allowing for dynamic andsemi-static objects to be removed from a formally static map. The proposedalgorithm is validated in a 3D simulated environment.

Better Together: Online Probabilistic Clique Change Detection in 3D Landmark-Based Maps