Robustness is key to engineering, automation, and science as a whole. However, the
property of robustness is often underpinned by costly requirements such as over …

To promote better performance-bandwidth trade-off for multi-agent perception, we propose a
novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and …

Multi-robot simultaneous localization and mapping (SLAM) is a crucial capability to obtain
timely situational awareness over large areas. Real-world applications demand multi-robot …

Locating devices on the road network is crucial for any location-based system. Accuracy of
map matching algorithms may highly affect the accuracy of any location-based service. This …

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

Place recognition is seen as a crucial factor to correct cumulative errors in Simultaneous
Localization and Mapping (SLAM) applications. Most existing studies focus on visual place …

Repetitive or ambiguous environment, where structures are highly similar and distinct
geometric features are not sufficient, is one of the critical and challenging scenarios for …

Accurate global localization is an essential ingredient for autonomous mobile robots (AMRs)
operating in enclosed or partially enclosed repetitive environments (eg, office corridors …

Filtering out outlier data associations between local maps can improve the robustness and
accuracy of multi-robot localization. When the overlap is low and the field of view difference …

Symmetric environment is one of the most intractable and challenging scenarios for mobile
robots to accomplish global localization tasks, due to the highly similar geometrical …