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SuperMap: A Spatio-Temporal SLAM System for Visual-Language Navigation
Shibo Zhao, Guofei Chen, Honghao Zhu +7
Robotic navigation in human environments requires a spatio-temporal semantic representation that can rec- oncile open-vocabulary perception with long-term environmental changes. Wh…
PRoID: Predicted Rate of Information Delivery in Multi-Robot Exploration and Relaying
Seungchan Kim, Seungjae Baek, Micah Corah +3
We address Multi-Robot Exploration and Relaying (MRER): a team of robots must explore an unknown environment and deliver acquired information to a fixed base station within a missi…
RAVEN: Resilient Aerial Navigation via Open-Set Semantic Memory and Behavior Adaptation
Seungchan Kim, Omar Alama, Dmytro Kurdydyk +5
Aerial outdoor semantic navigation requires robots to explore large, unstructured environments to locate target objects. Recent advances in semantic navigation have demonstrated op…
RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration
Omar Alama, Avigyan Bhattacharya, Haoyang He +6
Open-set semantic mapping is crucial for open-world robots. Current mapping approaches either are limited by the depth range or only map beyond-range entities in constrained settin…
PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration
Seungjae Baek, Brady Moon, Seungchan Kim +4
Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information g…
MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning
Narek Harutyunyan, Brady Moon, Seungchan Kim +3
Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-…