We present an approach for localization and semantic mapping in ambiguous scenarios by
incrementally maintaining a hybrid belief over continuous states and discrete classification …

We study the ambiguous data association problem confronting simultaneous localization
and mapping (SLAM), specifically for the autonomous exploration of environments lacking …

It is often desirable to capture and map semantic information of an environment during
simultaneous localization and mapping (SLAM). Such semantic information can enable a …

Performing collaborative semantic mapping is a critical challenge for cooperative robots to
enhance their comprehensive contextual understanding of the surroundings. This article …

We present an approach for multi-robot consistent distributed localization and semantic
mapping in an unknown environment, considering scenarios with classification ambiguity …

Indoor environments can typically be divided into places with different functionalities like
corridors, rooms or doorways. The ability to learn such semantic categories from sensor data …

Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting
loop closure under the circumstance of large viewpoint changes. In this article, we present …

Semantic interpretation of regions or entities is increasingly attracting the attention of
scholars, owing to its vast applicability in several disciplines. In this context, modern …

Data association in SLAM is fundamentally challenging, and handling ambiguity well is
crucial to achieve robust operation in real-world environments. When ambiguous …

Long-term autonomy of robots requires localization in an inevitably changing environment,
where the robots' knowledge about the surroundings are more or less uncertain. Inspired by …