AOV-GS is an active 3D Gaussian mapping system that uses frozen foundation models (SAM and CLIP) to guide exploration toward semantically informative regions. During coarse exploration, it re‑ranks geometrically promising views based on CLIP mask‑embedding novelty from SAM instance segmentation, biasing the robot’s trajectory. The system fits a lightweight scene‑specific language field offline to the collected CLIP embeddings, enabling open‑vocabulary text‑query segmentation on novel views and improving language‑field mIoU by 1.3–1.8 percentage points over geometry‑only planning on Replica and Matterport3D.
By Rodion Shkokov, Tatiana Zemskova, Dmitry Yudin
ActiveLang is an autonomous system that builds language‑annotated 3D maps by actively exploring environments. It uses a compact dual‑Gaussian representation to jointly reconstruct geometry, appearance, and open‑vocabulary semantics while adapting language features online. The planner selects informative viewpoints, reducing the number of observations and computational cost, and experiments on Replica and ScanNet++ show significant gains in 2D and 3D open‑vocabulary segmentation compared to existing baselines.
By Liyan Chen, Hairong Yin, Huangying Zhan, Yi Xu, Raymond A. Yeh, Philippos Mordohai
arXiv:2607. 20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently.
By Arjun Majumdar, Avinash Sooriyarachchi, Benjamin Tibi, Chris Bamford, Elliot Chane-Sane, Guillaume Lample, Khyathi Raghavi Chandu, Ludovic Ho Fuh, Mathieu Poiree, Olivier Duchenne, Rosalie Millner, Srijan Mishra, Theo Cachet, Thomas Chabal
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
Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode. Existing benchmarks usually evaluate indoor and outdoor navigation separately, and many abstract away robot execution, leaving exit finding, boundary traversal, adaptation, and kinodynamic failures underexplored.
arXiv:2609.17168v1 Announce Type: cross
Abstract: Autonomous systems require reliable place recognition for efficient and effective simultaneous localisation and mapping (SLAM). Traditional geometric...
By Mayowa Adebambo, Sebastian Donnelly, Armand Amaritei, Andrew Bradley, Alexander Rast
arXiv:2505. 21457v2 Announce Type: replace-cross Abstract: Active vision, also known as active perception, refers to actively selecting where and how to look in order to gather task-relevant information.
By Muzhi Zhu, Hao Zhong, Canyu Zhao, Zongze Du, Mingyu Liu, Zheng Huang, Anzhou Li, Hao Chen, Cheng Zou, Jingdong Chen, Ming Yang, Chunhua Shen
LangMap introduces a human‑verified benchmark for language‑conditioned goal navigation (LGN) that spans four hierarchical semantic levels—scene, room, region, and instance—within real‑world indoor 3D scans. The dataset, built on HM3D, contains 18K tasks with concise and detailed descriptions for 414 object categories, and its contrastive annotation protocol ensures high‑quality, discriminative region and instance labels. Evaluation shows that LangMap’s descriptions improve text‑to‑view matching accuracy by 23 points over GOAT‑Bench and achieve a 92.5% unique‑and‑correct match rate in an independent human audit, while a proposed RGB‑only baseline, PlaNaVid, attains top‑tier success rates without depth or 3D scene representations.
By Bo Miao, Weijia Liu, Jun Luo, Lachlan Shinnick, Jian Liu, Thomas Hamilton-Smith, Yuhe Yang, Zijie Wu, Vanja Videnovic, Feras Dayoub, Anton van den Hengel
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
arXiv:2608.23354v1 Announce Type: cross
Abstract: Autonomous indoor navigation requires both semantic understanding and precise geometric control. We propose OptiSight, a hybrid framework that combin...
By Alperen Avan, Jordi Sanchez-Riera
arXiv:2605.25059v4 Announce Type: replace
Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
By Ruoyu Wang, Yong Liu, Jiahan Li, Sheng Tao, Yuhang Lin, Yukai Ma
Metric-Bench introduces a new benchmark for Vision‑Language Models (VLMs) that focuses on metric‑spatial reasoning in indoor scenes by using in‑image reference objects with known dimensions. The accompanying MetricReasoner fine‑tuning recipe employs structured prompts and numerical rewards to implicitly learn 2D‑to‑3D mapping without camera intrinsics. Experiments show that this approach improves spatial metric understanding by 43.1 % over existing models and boosts downstream embodied tasks, while also delivering gains on general VLM benchmarks.
By Yuling Xi, Haokai Zhang, Muzhi Zhu, Hao Zhong, Zongze Du, Hengyu Zhao, Chenchen Jing, Yufei Yin, Bin Qin, Yongjie Yang, Zhenbo Luo, Hao Chen, Chunhua Shen