Long‐term operation of robots creates new challenges to Simultaneous Localization and
Map** (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes …

For robotic interaction in environments shared with other agents, access to volumetric and
semantic maps of the scene is crucial. However, such environments are inevitably subject to …

Inventory monitoring in homes, factories, and retail stores relies on maintaining data despite
objects being swapped, added, removed, or moved. We introduce Lifelong LERF, a method …

The global decrease in native pollinators poses a substantial challenge to agricultural
production and food security, particularly in malnutrition prone countries. There is a huge …

Lifelong SLAM considers long-term operation of a robot where already mapped locations
are revisited many times in changing environments. As a result, traditional graph-based …

Building semantic 3D maps is valuable for searching for objects of interest in offices,
warehouses, stores, and homes. We present a map** system that incrementally builds a …

The ability to maintain and continuously update geometric calibration parameters of a
mobile platform is a key functionality for every robotic system. These parameters include the …

We propose a lifelong 3D map** framework that is modular, cloud-native by design and
more importantly, works for both hand-held and robot-mounted 3D LiDAR map** systems …

Semi-open and chaotic environments of building sites are considered primary challenges for
the localization of mobile construction robots. To mitigate environmental limitations, an …

The real-world deployment of fully autonomous mobile robots depends on a robust
simultaneous localization and map** (SLAM) system, capable of handling dynamic …