arXiv:2606. 06721v1 Announce Type: cross Abstract: Robots that operate over extended periods should not merely visit space; they should progressively understand it.
By Junyu Mao, Sara Ayoubi, Vishnu D. Sharma, Ilija Had\v{z}i\'c, Matthew Andrews
arXiv:2608.29315v1 Announce Type: cross
Abstract: This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic...
By Christopher Tatsch, Yu Gu
The paper investigates how inaccuracies in pretrained occupancy networks affect active mapping robots that select camera viewpoints to reconstruct unknown 3D scenes. By fixing the planner and varying the occupancy representation—ranging from no completion to ground‑truth occupancy—the authors find that correcting false positives or false negatives alone does not reliably improve coverage, highlighting a disconnect between occupancy accuracy and planning performance. They propose a dynamic filtering strategy that retains predictions in unexplored space while suppressing unsupported occupancy based on online observations, which preliminarily shows it can steer viewpoint selection toward reachable surfaces that would otherwise remain unseen.
By Jiahui Zhang, Gongbo Liang, Yu Zhang
Hierarchical 3D scene graphs are a promising representation for high-level spatial reasoning in autonomous mobile platforms. However, existing extraction frameworks typically rely on purely local visual clustering or strict geometric heuristics, such as wall-separated rooms, which fail in open-plan or arbitrarily-structured environments.
arXiv:2510. 11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant semantics needed for safe navigation and goal-directed action.
By Subhransu S. Bhattacharjee, Hao Lu, Dylan Campbell, Rahul Shome
arXiv:2606. 18634v1 Announce Type: cross Abstract: To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots.
By Zecheng Yin, Benedict Jun Ma