Semantic Mapping

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8 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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ATLAS Navigator: Active Task-driven LAnguage-embedded Gaussian Splatting
Date pendingDexter Ong, Yuezhan Tao, Varun Murali +33D Gaussian Splatting Language-Embedded 3DGS
GRASP Laboratory, University of Pennsylvania, Philadelphia, PA 19104 USA · Texas A&M University, College Station, TX 77840 USA · University of California Riverside, Riverside, CA 92521 USA
FastMap: Real-Time Semantic Map Completion via Bitwise Masked Modeling
Date pendingYijie Deng, Shuaihang Yuan, Congcong Wen +2Semantic Mapping Masked Image Modeling
NYUAD Center for Artificial Intelligence and Robotics, Abu Dhabi, UAE. · New York University Abu Dhabi, Electrical Engineering, Abu Dhabi 129188, UAE. · New York University, Electrical & Computer Engineering Dept., Brooklyn, NY 11201, USA. +1

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ATLAS Navigator: Active Task-driven LAnguage-embedded Gaussian Splatting

Dexter Ong, Yuezhan Tao, Varun Murali, Igor Spasojevic+2

GRASP Laboratory, University of Pennsylvania, Philadelphia, PA 19104 USA · Texas A&M University, College Station, TX 77840 USA · University of California Riverside, Riverside, CA 92521 USA

3D Gaussian Splatting Language-Embedded 3DGS Robot Navigation Vision-Language Navigation Semantic Mapping

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We address the challenge of task-oriented navigation in unstructured and unknown environments, where robots must incrementally build and reason on informative metric-semantic maps in real time. Since tasks may require clarification or re-specification, it is necessary for the information in the map to be rich enough to enable generalization across a wide range of tasks. To effectively execute tasks specified in natural language, we propose a hierarchical representation built on language-embedded Gaussian splatting that enables both sparse semantic planning that lends itself to online operation and dense geometric representation for collision-free navigation. We validate the effectiveness of our method through real-world robot experiments on a ground robot equipped with only vision-based sensing. The experiments are conducted in both cluttered indoor and kilometer-scale outdoor environments, covering more than 2 km and 47,000 square meters of area. We achieve a competitive ratio of about 59% relative to the shortest possible path, demonstrating efficient exploration and semantic navigation in challenging real-world environments. Experiment videos and more details can be found on our project page: https://ongdexter.github.io/atlasnav.

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Latest papers 62

ATLAS Navigator: Active Task-driven LAnguage-embedded Gaussian Splatting

FastMap: Real-Time Semantic Map Completion via Bitwise Masked Modeling