3D Semantic Mapping

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25 papers5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

Oct 25, 2026

T-ConSG: A LiDAR-Based Real-Time Temporal Construction Scene Graph for Dynamic and Evolving Environments

Dynamic and continuously evolving environments present significant challenges for robotic mapping and scene understanding, particularly on construction sites where moving agents, temporary obstacles, and persistent structural changes coexist. While dynamic and long-term SLAM methods can detect motion and environmental changes, their outputs are predominantly metric representations that provide limited structure for higher-level spatial and temporal reasoning. This paper presents a LiDAR-based real-time temporal construction scene graph (T-ConSG) that jointly represents free-space regions, static objects, dynamic objects, and environmental events with spatial-relational and event-participation edges. The proposed framework incrementally constructs the graph from LiDAR-derived geometric evidence, maintaining persistent free-space and static-object information while tracking dynamic objects. Changes in accumulated spatial evidence are further represented as explicit installation and removal events, allowing the graph to capture both transient dynamics and persistent environmental evolution. We demonstrate the proposed representation through three complementary application studies: dynamics-aware navigation in a bricklaying testbed, construction progress monitoring on the ConSLAM dataset, and online change detection in a simulated construction environment. The results demonstrate that the proposed T-ConSG provides a unified representation for organizing spatial and temporal information and supports robotic reasoning across environments containing both short-term dynamics and persistent changes.

Yilong Chen, Yong K. Cho

Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

3D SGG Scene Graph Generation 3D Semantic Mapping Robotic Construction 3D Scene Change Detection

Oct 22, 2026

Point-Level Refinable Semantic Mapping for Spatial Understanding

Open-vocabulary 3D semantic maps have emerged as a key spatial memory representation for long-horizon robot autonomy, enabling robots to reason about objects, their spatial relationships, and past observations across time. However, existing object-centric semantic maps are primarily designed for static environments and rely on an append-only point integration strategy, where newly observed points are accumulated but previously integrated points are rarely reevaluated. Consequently, perception errors accumulate over time, and outdated points persist after objects move or disappear. Although recent dynamic mapping methods update object-level representations in changing scenes, they do not explicitly revise noisy or obsolete points within each object. We propose an online refinable semantic mapping framework that continuously refines object representations through probabilistic point-level confidence updating. Each object point maintains a confidence score that is updated online using multi-view temporal evidence: consistent observations reinforce reliable points, while inconsistent segmentation masks, depth measurements, and visibility cues suppress spurious and outdated points. Combined with object tracking and re-identification to preserve instance identities under motion and relocation, our framework enables object-centric semantic maps to be continuously refined rather than monotonically accumulated. Experiments demonstrate improved mapping accuracy over the previous method by 3.46% on Replica and 2.55% on ScanNet, show robustness in dynamic environments and under perception noise, and provide an effective spatial memory for object navigation and embodied question-answering with success rates of 82.4% and 76.7%.

Taimeng Fu, Yi Du, Zitong Zhan +4

Spatial AI & Robotics Lab, Department of Computer Science and Engineering, University at Buffalo, USA.

3D Semantic Mapping Semantic Mapping Robotic Spatial Memory 3D Scene Understanding

Oct 21, 2026

AOV-GS: Active Open-Vocabulary Semantic Mapping with 3D Gaussian Splatting

Robots deployed in human-centered environments need maps that support both geometric navigation and open-vocabulary semantic queries beyond a fixed label set. A key challenge is enriching such maps with semantic information without relying on manually annotated segmentation masks or closed-set semantic categories. Existing geometry-driven active mapping maximizes coverage but treats semantics as a passive by-product of the collected trajectory, whereas active semantic mapping typically depends on supervised semantic segmentation. We present AOV-GS, an active 3D Gaussian mapping system that guides exploration using frozen foundation models. During coarse exploration, AOV-GS re-ranks geometrically promising candidate views according to CLIP mask-embedding novelty computed from SAM instance segmentation, biasing exploration toward semantically informative regions. Frozen SAM and CLIP provide instance masks and language-aligned embeddings throughout mapping, while a lightweight scene-specific language field is fitted offline directly to the collected CLIP embeddings rather than manually annotated masks, enabling open-vocabulary text-query segmentation on novel views. In a controlled comparison using the same SAM-CLIP perception stack, AOV-GS improves average language-field mIoU over geometry-only active planning by 1.3 percentage points on Replica (34.6% on eight scenes) and by 1.8 points on Matterport3D (23.1% on five scenes). With CorrCLIP, AOV-GS reaches 35.6% on Replica, 2.0 points above the matched Geometry-based + CorrCLIP baseline. Novel-view PSNR remains comparable to the geometry-only baseline on both datasets while depth L1 improves on Replica. Code is available at https://github.com/OptimusFaber/AOV-GS.

Rodion Shkokov, Tatiana Zemskova, Dmitry Yudin

Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Russia · AIRI, Moscow, Russia

Semantic Mapping Open-Vocabulary Semantic Mapping 3D Semantic Mapping 3D Mapping Next-Best-View Planning 3DGS Segmentation

Oct 7, 2026

ActiveLang: Active Open-Vocabulary 3D Mapping with Semantic-Uncertainty-Guided Exploration

As robots increasingly assist humans with diverse tasks, they need both geometric and semantic understanding of their surroundings. Moreover, robots often operate in unfamiliar environments and take on new tasks without knowing the relevant concepts ahead of time. This motivates language-annotated 3D maps that support open-vocabulary scene understanding and human-robot interaction. We introduce ActiveLang, an autonomous system for active open-vocabulary 3D mapping with semantic-uncertainty-guided exploration. ActiveLang performs online language-feature adaptation on a compact dual-Gaussian representation to jointly reconstruct scene geometry, appearance, and open-vocabulary semantics with modest memory overhead. Its planner efficiently selects informative viewpoints, enabling effective mapping with fewer observations and lower computational cost. Experiments on Replica and ScanNet++ demonstrate substantial improvements in 2D and 3D open-vocabulary segmentation over both online and offline baselines, highlighting that actively exploring scenes builds language-annotated 3D maps more efficiently.

Liyan Chen, Hairong Yin, Huangying Zhan +3

Stevens Institute of Technology · Purdue University · Goertek Alpha Labs

3D Semantic Mapping 3D Mapping 3D Scene Representation Open-Vocabulary 3D Segmentation 3D Reconstruction Active Perception+1

Oct 6, 2026

OpenSplatGraph: From Dense Semantic Maps to Structured Scene Graphs for Open-Vocabulary Robot Perception

Dense 3D mapping with semantic understanding is essential for robotic perception in complex environments. Recent 3D Gaussian Splatting-based mapping approaches enable high-fidelity geometry and efficient open-vocabulary perception, but typically represent semantics as unstructured feature fields that limit object-centric reasoning. In contrast, 3D scene graphs explicitly model objects and their relationships for structured reasoning, but are commonly constructed from sparse geometric representations that do not fully exploit dense semantic maps. In this work, we present OpenSplatGraph, a unified framework that constructs persistent 3D scene graphs directly from an online Gaussian-based open-vocabulary semantic map. The proposed framework augments the dense semantic map with a reliability-aware semantic field that maintains lightweight observation statistics for confidence-aware, query-conditioned object extraction. Extracted object instances are associated with persistent graph nodes, allowing object attributes and relationships to be incrementally updated across observations and queries. By tightly coupling dense semantic mapping with persistent object-centric representations, our framework supports both language-guided object grounding and structured relational reasoning while preserving the geometric fidelity of Gaussian-based mapping. Comprehensive evaluations on standard 3D scene understanding benchmarks and real-world robotic experiments demonstrate that OpenSplatGraph achieves competitive performance for online open-vocabulary perception and downstream robotic tasks. Project page: https://csiro-robotics.github.io/OpenSplatGraph.

Binh Long Nguyen, Kien Nguyen, Clinton Fookes +1

School of Electrical Engineering and Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia · CSIRO Robotics, CSIRO, Brisbane, QLD 4069, Australia

Language-Embedded 3DGS Open-Vocabulary 3D Segmentation 3D SGG 3D Visual Grounding 3D Semantic Mapping

Sep 25, 2026

TRACKGRAPH: Online Open-Vocabulary 3D Scene Graphs via Image-Space Tracking

Open-vocabulary 3D maps enable robots to reason about previously unknown environments using natural language. However, existing systems typically segment every incoming image, associate detections with persistent 3D segments, and frequently perform costly Vision-Language (VL) inference. We present TRACKGRAPH, an online open-vocabulary system that maintains short-term 2D mask identity directly in the image stream before fusing segments into 3D. FastSAM masks and CLIP features are computed at sparse keyframes, while dense DINOv3 features are used to propagate masks at a high rate in between. The resulting tracked masks are fused into a class-agnostic 3D segment layer within a hierarchical scene graph, with 3D association handling tracking interruptions and long-term revisits. Compact multi-view CLIP embeddings enable open-vocabulary retrieval. Across Replica, ScanNet++, and HM3D, TRACKGRAPH achieves competitive open-vocabulary segmentation and retrieval against state-of-the-art mapping methods, including the highest synonym frequency on Replica (0.50). On the same NVIDIA A100, it is 1.7x faster and uses 3.3x less GPU memory than ViT-H OVI-MAP. Real-world quadruped deployments demonstrate onboard scene graph construction and object search at 7.5Hz, while recorded drone data is used to test the method under aerial viewpoints.

Peder Borge Hellesylt, Albert Gassol Puigjaner, Kostas Alexis +1

Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Open-Vocabulary 3D Segmentation 3D Semantic Mapping 3D SGG 3D Scene Representation VLMs for Robotics Visual Object Tracking

Sep 23, 2026

NaviScale: Generating Large-Scale Semantic Map Datasets for Object Navigation

Embodied navigation requires spatial representations that generalize across unseen environments, yet collecting large amounts of annotated data from real 3D environments is difficult. We propose NaviScale for semantic-map-based object navigation (ObjectNav), whose predictor can be trained on pairs of partial and complete semantic maps without reconstructing a complete 3D environment for every training sample. The framework generates large-scale semantic map training data by composing floorplans of real homes with room-level semantic and obstacle maps extracted from MP3D and HM3DSem. NaviScale increases data diversity in two ways: inter-room scaling increases floorplan-level structural diversity, while intra-room scaling fills each fixed floorplan with different combinations of room maps matched by room category. Visibility through Ray Casting (VisRC) converts the composed maps into partial observations that account for field of view, sensing range, and occlusion. The resulting dataset contains 192,000 semantic maps generated from 24,000 floorplans associated with 12,794 properties. With 300k training iterations and the training and inference settings described in this paper, the system reaches 64.3% SR and 34.8% SPL on HM3D, together with 43.1% SR and 16.8% SPL on MP3D, without changing the prediction architecture. Additional experiments evaluate the quality of the composed maps, the effects of semantic-segmentation errors, and deployment on a physical robot.

Chuanlin Lan, Yanwei Zheng, Yuxi Jing +6

School of Computer Science and Technology, Shandong University, Qingdao, China

Robot Navigation Synthetic Data Generation for Robotics Indoor Robot Navigation Object-Goal Navigation 3D Semantic Mapping

Sep 17, 2026

PerSeM: Persistent Semantic Memory for Long-Horizon Open-Vocabulary UAV Mapping

Open-vocabulary segmentation enables rich semantic perception for UAVs, but frame-wise predictions can remain temporally inconsistent across repeated observations and changing viewpoints. We present PerSeM, a training-free persistent semantic memory framework for long-horizon open-vocabulary UAV mapping. PerSeM associates frame-wise semantic observations with persistent world-space voxels and constructs a majority-based semantic memory, which is conservatively refined through history-preserving spatial refinement, trust-aware replay, and context-guided verification. Experiments on the Forest and UAVScenes benchmarks show that persistent 3D memory provides substantial gains in semantic correctness and temporal stability over frame-wise predictions. Beyond this strong persistent-memory baseline, PerSeM provides consistent additional improvements, improving both semantic accuracy and temporal stability across all five evaluated UAVScenes sequences. Analysis using regions identified independently of the final PerSeM predictions further shows that these gains are concentrated in semantically difficult and temporally unstable regions, where majority-based memory is most likely to remain uncertain. These results demonstrate that persistent 3D aggregation provides a strong foundation for long-horizon semantic mapping, while conservative refinement of uncertain memory states can provide additional improvements without retraining or additional neural-network inference.

Saurbh Singh Jamwal, Ganesh Ramakrishnan

Department of Computer Science and Engineering Indian Institute of Technology Bombay

Semantic Mapping Open-Vocabulary 3D Segmentation Semantic Occupancy Mapping Semantic Segmentation Open-Vocabulary Semantic Mapping UAV Remote Sensing+1

Sep 4, 2026

Open-Set 3D Scene Graphs for Field Robotics: An Outdoor Case Study

Three-dimensional scene graphs (3DSGs) have emerged as a promising approach for building geometrically grounded, semantically informed, hierarchical general-purpose maps to support high-level robotic reasoning. However, the behavior of 3DSGs in real-world outdoor deployments remains poorly understood, particularly when combined with open-set vision-language models (VLMs). In this field report, we analyze the components common to most 3DSG representations across five outdoor robotic datasets to characterize challenges that arise in complex outdoor environments. Using the recently proposed Terra 3DSG as a case study, we investigate semantic point embeddings, place-node graph navigation, region-level understanding, and memory size across the five diverse datasets. We additionally introduce novel consistency metrics to evaluate whether semantic and structural graph properties remain stable across repeated traversals of the same environment. Our analysis reveals that outliers and multiple modes are common in VLM point embeddings across all tested datasets with outlier ratios above

0.1

for around

30\%

of points. We demonstrate the feasibility of outdoor 3DSGs for navigation-based object retrieval, achieving success rates near

70\%

, though performance is limited by traversability failures and inefficient routing, with trajectories averaging approximately

66\%

suboptimal path efficiency. Region-level understanding remains challenging in complex natural environments, with low average F1 scores around

0.359

. Overall, our results show that outdoor 3DSGs can maintain compact (less than

600

MB for multi-kilometer trajectories) and relatively consistent large-scale environment representations, while highlighting open challenges in handling multiple semantic modes, incorporating traversability into graph structures, and improving higher-level region understanding.

Chad R. Samuelson, Gabriel R. Slade, Joshua G. Mangelson

Brigham Young University

3D Scene Representation 3D Semantic Mapping 3D Scene Understanding 3D VLMs Off-Road Robot Navigation

Aug 18, 2026

OVIP-SG: Open-Vocabulary Instance-Preserving Scene Graphs for Mapping and Retrieval of Small, Fine-Grained Objects

Integrating open-vocabulary perception into object-level 3D scene graphs is a double-edged sword. While vision-language detectors recover long-tail categories and small, fine-grained objects overlooked by closed-set models, they also tend to fragment large surfaces and merge small objects into larger neighboring objects, compromising instance-level consistency and undermining mapping fidelity. Moreover, existing methods struggle to retrieve previously unmapped targets or determine whether a queried object is absent, hindering robust embodied open-world navigation and exploration. We present OVIP-SG, a unified framework for instance-preserving semantic mapping, functional scene partitioning, and language-guided small, fine-grained object retrieval. OVIP-SG uses a vision-language model (VLM) to enumerate scene-specific categories for robust open-world detection. Symmetric 3D Intersection over Union (IoU) association and area-weighted feature fusion preserve small independent instances, while VLM-inferred object functions partition scenes into compact functional search regions. A four-stage cascaded retrieval pipeline further incorporates voxel voting and determines target absence from exploration coverage. Under a unified evaluation protocol on Replica, OVIP-SG outperforms ConceptGraphs by 6.31 points in class-mean accuracy (mAcc) and 5.15 points in frequency-weighted mIoU (F-mIoU) while achieving a class-agnostic native-instance Panoptic Quality (PQ) of 0.398. It reduces the search area to 21.8% of the indoor floor space and reaches 0.773 balanced accuracy for object-presence classification. Real-world robotic experiments further demonstrate its practical effectiveness. Code is available at https://github.com/Agibot-Spatial-Intelligence/OVIP-SG.

Tianjing Hao, Haiyu Lan, Angsong Li +6

1Xi’an Jiaotong University · 2AgiBot

3D Semantic Mapping 3D SGG 3D Scene Understanding Vision-Language Retrieval

Aug 10, 2026

RoboSeg: Online Part-Level Semantic Reconstruction for Robotic Manipulation via a Single Eye-in-Hand Camera

Robotic manipulation requires perception systemsthat identify actionable parts such as handles, rims, triggers,and tool tips, not merely object categories or point clouds. This paper presents RoboSeg, a part-level semantic reconstructionsystem that links vision-language model (VLM) functional-partdiscovery, asynchronous online RGB-D semantic reconstruc-tion, and task-oriented grasp generation without requiring CAD models or pre-scanned meshes. RoboSeg queries a VLM onthe initial RGB observation to obtain compact functional part prompts, then scans with two asynchronous streams: a high-frequency geometry thread for RGB-D odometry and truncated signed distance function (TSDF) fusion, and a keyframe-triggered semantic thread for SAM3 part masks. Projectedmasks are fused by voxel-level temporal voting into a persistentpart-labeled point cloud; RoboSeg uses this map to assign AnyGrasp 6-DoF candidates to semantic parts and select grasps consistent with the task-relevant part label. RoboSeg reaches 83.4% mean part intersection-over-union (mIoU) over manually labeled objects; in a 24-trial physical pilot across fourobjects and eight tasks, the selected grasp contacts the requestedpart in all trials and achieves 21/24 combined task successes.These results characterize RoboSeg as a semantic indexing layerfor task-conditioned manipulation, with AnyGrasp retained asthe proposal generator.

Zhaochen Lan, Mengxiang Lin

School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China

Robotic Grasping 3D Semantic Mapping 3D Part Segmentation Robotic Manipulation 3D Scene Understanding

Aug 7, 2026

M2-SMap: Memory-Efficient Semantic Mapping with Hierarchical Multi-Model Representation

Dense point cloud maps, as a typically used mapping representation, are difficult to deploy on resource-constrained robots because their memory consumption grows rapidly with scene scale. Although compact single-model representations reduce memory cost, their fixed geometric expressiveness is insufficient for structurally diverse environments. Existing multi-model methods improve representational flexibility, yet their feature extraction and model selection are often dominated by local geometry, which can cause overfitting and adhesion between objects. To address these issues, this paper presents M2-SMap, a memory-efficient semantic mapping framework based on hierarchical multi-model representation. First, a hierarchical geometric decomposition partitions RGB-D point clouds into compact Gaussian components. Then, a projection-guided semantic annotation mechanism assigns instance identities to each component. Subsequently, these annotations are incorporated into an object-aware Gaussian fusion strategy. Furthermore, a multi-scale feature extraction strategy separates large planar regions, semantic objects, and complex residual structures, which are respectively represented by bounded planes, object-level superquadrics, and GMM primitives. Experiments on three RGB-D sequences show that M2-SMap runs in real time at no less than 29.37 Hz while achieving the lowest primitive count, with an average reduction of 18.7% over the best baseline. It also reduces the mean per-frame number of measured inter-object adhesion cases from 2.808 to 0, demonstrating efficient and semantically consistent scene representation.

QiYing Deng, ZhongLai Wang, Yuan Gao +1

University of Electronic Science and Technology of China · State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, China

Superquadric Representation 3D Scene Representation Semantic Mapping 3D Semantic Mapping

Jul 30, 2026

MonoVoc: Decoupling Geometry and Semantics for Lightweight Monocular Open-Vocabulary 3D Gaussians

Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstructed environments using natural language. However, current 3D Gaussian frameworks are often bottlenecked by restrictive multiview capture requirements, costly scene-specific optimization, and the massive memory overhead of storing dense language features. We present a novel, training-free pipeline that fundamentally reimagines this paradigm by explicitly decoupling 3D geometric reconstruction from semantic integration. Given a standard monocular video sequence as input, our method efficiently outputs a compact, highly interpretable, and fully searchable object-level semantic Gaussian map. Rather than entangling heavy language embeddings within the mapping loop, we extract geometry independently and ground semantics through a lightweight, modular post-processing framework. Extensive evaluations on the Replica dataset demonstrate that this decoupled architecture preserves strong rendering fidelity and competitive segmentation accuracy. Crucially, by replacing dense per-Gaussian storage with modular, object-level semantic embeddings, our approach delivers an order-of-magnitude reduction in memory usage compared to SOTA baselines. This provides a highly efficient, scalable, and practical solution for open-vocabulary 3D retrieval and question answering directly from everyday monocular video.

Pouya Ardekhani, Zahra Dehghanian, Morteza Abolghasemi +1

Sharif University of Technology, Tehran, Iran

3D Gaussian Splatting 3D Semantic Mapping Open-Vocabulary 3D Segmentation Monocular 3D Reconstruction 3D Scene Understanding

Jul 17, 2026

Vision-Language-Motion Maps: An Open-Vocabulary, Uncertainty-Aware, Queryable Motion Attribute for 3D Scene Maps

Open-vocabulary 3D maps let robots answer language queries about what and where, but they assume a static world and cannot answer queries about how scene elements behave. We introduce Vision-Language-Motion Maps (VLMM), an open-vocabulary, language-queryable 3D map - queried through a rule-based intent router over open-vocabulary object nouns, not a general natural-language interface - in which each element carries a fused motion attribute: a VLM/LLM semantic movability prior combined with geometrically observed cross-frame motion, together with a per-element uncertainty. Queries reduce to attribute filters that distinguish what has been seen to move, what could move but has not, and what stays still. On a controlled simulator benchmark with exact ground truth (AI2-THOR, three scene types) we show through ablation that the schema fields are non-substitutable: a semantic-only baseline fails motion queries even with strong features, and neither motion field substitutes for the other (the prior cannot answer "what is moving," observed motion cannot answer "what could move"). On real dynamic RGB-D (TUM and Bonn, six sequences) we show the uncertainty channel - our key difference from prior fused-motion work - consistently improves moving-vs-static average precision and reduces false motion flags, and that it is robust to estimated (noisy) poses. The raw confidence is not calibrated, but post-hoc isotonic calibration reaches an expected calibration error of 0.10. VLMM is a representation contribution: the closest prior maps each lack at least one of the four properties - open-vocabulary, language-queryable, fused prior-and-observed motion, and per-element uncertainty - that our combination provides.

Dibyendu Ghosh, Ayushi Shakya

Rekise Marine, Bangalore, India

Uncertainty Quantification 3D Scene Representation Robotic Perception 3D Mapping 3D Semantic Mapping

Jun 30, 2026

Think While You Map: Asynchronous Vision-Language Agents for Incremental 3D Scene Graphs

Open-vocabulary 3D scene graph methods typically operate in two stages: first reconstruct, then enrich with vision-language models, leaving the graph unqueryable during exploration. We argue that this sequential coupling is unnecessary and propose an asynchronous architecture in which lightweight online mapping runs concurrently with heavyweight semantic refinement. A probabilistic voxel-based backbone maintains stable object identities incrementally, while background VLM agents progressively enrich the graph. This framework resolves duplicate object tracks through semantic loop closure, attaches fine-grained visual attributes and derives spatial relations between objects. A multi-target frame scheduler amortizes VLM cost by selecting a small set of informative frames that jointly cover multiple targets. The resulting scene graph is queryable during exploration and grows in semantic richness over time. Our method matches or outperforms existing open-vocabulary 3D scene graph methods on semantic segmentation (ScanNet, Replica) and surpasses the prior state-of-the-art across three visual grounding benchmarks (Sr3D+, Nr3D, ScanRefer) by 15.3 to 18.8 [email protected]. Project page: https://denizbickici.github.io/thinkgraphs/

Deniz Bickici, Michael Pabst, Shohei Mori +1

University of Stuttgart, Germany · IMPRS-IS, Germany · Graz University of Technology, Austria

Open-Vocabulary 3D Segmentation 3D SGG 3D Visual Grounding 3D Semantic Mapping 3D Mapping

Jun 24, 2026

RoboAtlas: Contextual Active SLAM

We present RoboAtlas, a contextual Active SLAM framework that adaptively balances geometric exploration and semantic reasoning using a scalable 3D semantic mapping system, OpenRoboVox. RoboAtlas integrates frontier exploration, global semantic-map reasoning, and egocentric VLM-based reasoning through a contextual multi-armed bandit that transitions from exploration to semantically guided navigation as scene understanding improves. We evaluate the system in simulation and on a Unitree Go2 robot in large-scale real-world environments exceeding 1800 m2 with approx. 30k mapped semantic instances, achieving a 100% task success rate. On the GOAT-Bench "Val Unseen" benchmark, RoboAtlas achieves state-of-the-art performance with highest reported success rate (SR) of 90.6%, using GPT-4o, improving over the strongest prior baseline by 17.8 percentage points in SR. Using the much smaller Qwen2.5-VL-7B model, it still achieves 88.8% SR, outperforming all baselines using GPT-4o in SR, and revealing the importance of the information gained by our semantic mapping framework over simply replacing the underlying foundation model. The results demonstrate that grounding foundation models with large-scale 3D semantic maps enables robust and efficient contextual Active SLAM.

Alexander Schperberg, Shivam K. Panda, Abraham P. Vinod +3

Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA · Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA · Mitsubishi Electric Corporation, Japan

Simultaneous Localization and Mapping Robot Navigation Semantic Mapping Autonomous Robot Exploration 3D Semantic Mapping

Jun 7, 2026

Language as a Sensor: Calibrated Spatial Belief Estimation in 3D Scenes from Natural Language

Robots deployed in human-centric environments routinely receive natural-language descriptions of spatial information ("I left my backpack on the table") that reference parts of the world beyond their perceptual field of view. Traditional metric-semantic mapping ignores this signal, while off-the-shelf multimodal models remain limited in 3D spatial reasoning and are not directly amenable to fusion with other sensor modalities. To convert language observations into a calibrated spatial distribution, we train a Language Sensor Model (LSM) that maps each utterance and its scene-graph context to a multimodal distribution, with mixture weights encoding referential ambiguity (e.g., which table) and component covariances encoding spatial uncertainty (e.g., where on the table the target lies). We then introduce VL-Map (Vision-Language Metric-Semantic Mapping), a probabilistic framework that treats these language predictions as stochastic observations and fuses them with onboard perception within a unified belief map. On the VLA-3D benchmark as well as on a real-world mobile robot, LSM is the only language predictor whose covariance estimates remain within the calibrated regime; fused into VL-Map, it leads to more accurate predictions of the target object location (about 70% more probability mass on the true target compared to the strongest foundation-model baseline). Project website: https://aryan-naveen.github.io/language-as-a-sensor/

Aryan Naveen, Jason Xinyu Liu, Luca Carlone +1

MIT Laboratory for Information & Decision Systems · MIT Computer Science & Artificial Intelligence Laboratory

Vision-Language Grounding Semantic Mapping Multimodal Sensor Fusion Multimodal Understanding 3D Semantic Mapping

Jun 1, 2026

SAVMap: Structure-Aided Visual Mapping of Large-Scale 2.5D Manhattan Wireframes from Panoramic Video

Precise 3D representations of industrial environments enable tasks such as robot localization and digital twin generation. We propose SAVMap, a method for generating a semantic wireframe map of warehouse shelf and light structures using only a panoramic video camera as the sensor input. Sequences of rectified images with shelf and ceiling-facing views are extracted from a panoramic video captured along the warehouse aisles. Using a semantic segmentation network front end, a set of sparse, semantic structure feature points (e.g., corners of shelf structures, centers of lights) are extracted from each image and tracked across the sequences. By accounting for real-world geometric relationships among the points such as Manhattan grids, a constrained structure-from-motion algorithm yields the 3D points that form a wireframe map. We demonstrate the scalability and accuracy of our proposal in a warehouse with 46 shelving rows, each with faces spanning 55,m by 7,m. From an hour of panoramic video content, we create wireframe maps for over 5000 shelf elements across the rows, achieving an aggregate mean absolute error of 4.8,cm with respect to ground-truth.

Howard Huang, Bharath Surianarayanan, Keifer Lee +2

3D Reconstruction 3D Scene Representation Semantic Mapping Structure from Motion 3D Semantic Mapping

May 17, 2026

Mono-Hydra++: Real-Time Monocular Scene Graph Construction with Multi-Task Learning for 3D Indoor Mapping

Autonomous agile robots need more than metric geometry: they must understand objects, rooms, places, and spatial relations for search, inspection, exploration, and human-robot interaction. Conventional metric maps support localization and collision avoidance, but do not provide this semantic and relational structure. 3D scene graphs address this gap by connecting geometry with object-level and room-level understanding. Building such representations on agile platforms remains difficult because aerial and lightweight robots operate under strict payload, power, and compute limits, making RGB-D cameras and LiDAR sensors impractical for many onboard settings. We present Mono-Hydra++, a real-time monocular RGB plus IMU pipeline for indoor metric-semantic mapping and hierarchical 3D scene graph construction. The system combines M2H-MX, a DINOv3-based multi-task model for depth and semantics, with a deep-feature visual-inertial odometry front end, sparse predicted depth constraints in the VIO-derived pose graph, semantic masking for dynamic regions, and pose-aware temporal alignment before volumetric fusion in the Mono-Hydra backend. On the Go-SLAM ScanNet evaluation subset, Mono-Hydra++ achieves 1.6% lower average trajectory error than the strongest RGB-D baseline in our comparison, while using only monocular RGB plus IMU input. On calibrated 7-Scenes, it improves average ATE by 29.8% over the strongest competing calibrated baseline. We further validate Mono-Hydra++ in a real ITC building deployment using RealSense RGB plus IMU and demonstrate embedded feasibility by deploying the ONNX/TensorRT FP16 M2H-MX-L perception model at 25.53 FPS on a Jetson Orin NX 16GB. These results show that Mono-Hydra++ can provide real-time metric-semantic mapping and scene graph construction for resource-constrained robotic platforms without relying on active depth sensors.

U. V. B. L. Udugama, George Vosselman, Francesco Nex

Department of Earth Observation Science, University of Twente, Enschede, 7522 NH, The Netherlands

3D Semantic Mapping 3D SGG Visual-Inertial SLAM Robotic Perception 3D Scene Understanding

May 5, 2026

FUS3DMaps: Scalable and Accurate Open-Vocabulary Semantic Mapping by 3D Fusion of Voxel- and Instance-Level Layers

Open-vocabulary semantic mapping enables robots to spatially ground previously unseen concepts without requiring predefined class sets. Current training-free methods commonly rely on multi-view fusion of semantic embeddings into a 3D map, either at the instance-level via segmenting views and encoding image crops of segments, or by projecting image patch embeddings directly into a dense semantic map. The latter approach sidesteps segmentation and 2D-to-3D instance association by operating on full uncropped image frames, but existing methods remain limited in scalability. We present FUS3DMaps, an online dual-layer semantic mapping method that jointly maintains both dense and instance-level open-vocabulary layers within a shared voxel map. This design enables further voxel-level semantic fusion of the layer embeddings, combining the complementary strengths of both semantic mapping approaches. We find that our proposed semantic cross-layer fusion approach improves the quality of both the instance-level and dense layers, while also enabling a scalable and highly accurate instance-level map where the dense layer and cross-layer fusion are restricted to a spatial sliding window. Experiments on established 3D semantic segmentation benchmarks as well as a selection of large-scale scenes show that FUS3DMaps achieves accurate open-vocabulary semantic mapping at multi-story building scales. Additional material and code will be made available: https://githanonymous.github.io/FUS3DMaps/.

Timon Homberger, Finn Lukas Busch, Jesús Gerardo Ortega Peimbert +2

Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology, Sweden

Open-Vocabulary 3D Segmentation 3D Semantic Segmentation Semantic Mapping 3D Scene Understanding 3D Semantic Mapping

May 3, 2026

Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning

Understanding the geometric and semantic structure of environments is essential for embodied navigation and reasoning. Existing semantic mapping methods trade off between explicit geometry and multi-scale semantics, and lack a native interface for large models, thus requiring additional training of feature projection for semantic alignment. To this end, we propose the multi-scale Gaussian-Language Map (GLMap), which introduces three key designs: (1) explicit geometry, (2) multi-scale semantics covering both instance and region concepts, and (3) a dual-modality interface where each semantic unit jointly stores a natural language description and a 3D Gaussian representation. The 3D Gaussians enable compact storage and fast rendering of task-relevant images via Gaussian splatting. To enable efficient incremental construction, we further propose a Gaussian Estimator that analytically derives Gaussian parameters from dense point clouds without gradient-based optimization. Experiments on ObjectNav, InstNav, and SQA tasks show that GLMap effectively enhances target navigation and contextual reasoning, while remaining compatible with large-model-based methods in a zero-shot manner. The code is available at https://github.com/sx-zhang/GLMap.

Sixian Zhang, Yiyao Wang, Xinhang Song +3

State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing · University of Chinese Academy Sciences, Beijing · Institute of Computing Technology, Chinese Academy of Sciences, Beijing

3D Semantic Mapping Embodied Navigation 3D Scene Representation Object-Goal Navigation 3D Scene Understanding

Mar 29, 2026

GS3LAM: Gaussian Semantic Splatting SLAM

Recently, the multi-modal fusion of RGB, depth, and semantics has shown great potential in dense Simultaneous Localization and Mapping (SLAM). However, a prerequisite for generating consistent semantic maps is the availability of dense, efficient, and scalable scene representations. Existing semantic SLAM systems based on explicit representations are often limited by resolution and an inability to predict unknown areas. Conversely, implicit representations typically rely on time-consuming ray tracing, failing to meet real-time requirements. Fortunately, 3D Gaussian Splatting (3DGS) has emerged as a promising representation that combines the efficiency of point-based methods with the continuity of geometric structures. To this end, we propose GS3LAM, a Gaussian Semantic Splatting SLAM framework that processes multimodal data to render consistent, dense semantic maps in real-time. GS3LAM models the scene as a Semantic Gaussian Field (SG-Field) and jointly optimizes camera poses and the field via multimodal error constraints. Furthermore, a Depth-adaptive Scale Regularization (DSR) scheme is introduced to resolve misalignments between scale-invariant Gaussians and geometric surfaces. To mitigate catastrophic forgetting, we propose a Random Sampling-based Keyframe Mapping (RSKM) strategy, which demonstrates superior performance over common local covisibility optimization methods. Extensive experiments on benchmark datasets show that GS3LAM achieves increased tracking robustness, superior rendering quality, and enhanced semantic precision compared to state-of-the-art methods. Source code is available at https://github.com/lif314/GS3LAM.

Linfei Li, Lin Zhang, Zhong Wang +1

School of Software Engineering, Tongji University Shanghai, China · Department of Automation, Shanghai Jiaotong University Shanghai, China

3D Semantic Mapping 3DGS SLAM 3D Gaussian Splatting 3D Scene Understanding

Mar 27, 2026

OVI-MAP:Open-Vocabulary Instance-Semantic Mapping

Incremental open-vocabulary 3D instance-semantic mapping is essential for autonomous agents operating in complex everyday environments. However, it remains challenging due to the need for robust instance segmentation, real-time processing, and flexible open-set reasoning. Existing methods often rely on the closed-set assumption or dense per-pixel language fusion, which limits scalability and temporal consistency. We introduce OVI-MAP that decouples instance reconstruction from semantic inference. We propose to build a class-agnostic 3D instance map that is incrementally constructed from RGB-D input, while semantic features are extracted only from a small set of automatically selected views using vision-language models. This design enables stable instance tracking and zero-shot semantic labeling throughout online exploration. Our system operates in real time and outperforms state-of-the-art open-vocabulary mapping baselines on standard benchmarks.

Zilong Deng, Federico Tombari, Marc Pollefeys +2

ETH Zurich · University of Zurich · Google +2

3D Semantic Mapping Semantic Mapping Open-Vocabulary 3D Segmentation 3D Instance Segmentation

Aug 8, 2025

VLEM: Real-Time 3D Vision-Language Embedding Mapping

Semantic scene understanding in robotics requires representations that are both metric-accurate and queryable via natural language in real-time. While recent Vision-Language Models enable powerful 2D image-text alignment, their integration into real-time 3D mapping systems remains challenging due to their requirements on ground truth poses, computational cost, and memory constraints. We present VLEM (Vision-Language Embedding Mapping), a real-time framework for integrating pixel-aligned 2D vision-language embeddings from various backends into a globally consistent, metric-accurate 3D representation, requiring only a raw RGB-D stream. Compared to ConceptFusion, Open-Fusion, and RayFronts, VLEM provides better open-set segmentation performance and a more compact representation. We further demonstrate VLEM's versatility in interactive real-time robotic manipulation tasks and mobile mapping scenarios.

Christian Rauch, Björn Ellensohn, Linus Nwankwo +2

Technical University of Leoben, Franz Josef-Straße 18, 8700 Leoben, Austria

Open-Vocabulary 3D Segmentation 3D Scene Representation Robotic Perception 3D VLMs 3D Semantic Mapping 3D Mapping

Nov 13, 2024

Voxeland: Probabilistic Instance-Aware Semantic Mapping with Evidence-based Uncertainty Quantification

Robots in human-centered environments require accurate scene understanding to perform high-level tasks effectively. This understanding can be achieved through instance-aware semantic mapping, which involves reconstructing elements at the level of individual instances. Neural networks, the de facto solution for scene understanding, still face limitations such as overconfident incorrect predictions with out-of-distribution objects or generating inaccurate masks. Placing excessive reliance on these predictions makes the reconstruction susceptible to errors, reducing the robustness of the resulting maps and hampering robot operation. In this work, we propose Voxeland, a probabilistic framework for incrementally building instance-aware semantic maps. Inspired by the Theory of Evidence, Voxeland treats neural network predictions as subjective opinions regarding map instances at both geometric and semantic levels. These opinions are aggregated over time to form evidence, and are formalized through a probabilistic model. This enables us to quantify uncertainty in the reconstruction process, facilitating the identification of map areas requiring improvement (e.g. reobservation or reclassification). As a possible strategy to exploit this uncertainty quantification, we incorporate a Large Vision-Language Model (LVLM) to perform semantic level disambiguation for instances with high uncertainty. Results from the standard benchmarking on the publicly available SceneNN dataset demonstrate that Voxeland outperforms state-of-the-art methods, highlighting the benefits of incorporating and leveraging both instance- and semantic-level uncertainties to enhance reconstruction robustness. This is further validated through qualitative and quantitative experiments conducted on the real-world ScanNet dataset.

Jose-Luis Matez-Bandera, Pepe Ojeda, Javier Monroy +2

Machine Perception and Intelligent Robotics (MAPIR) Group, Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, Campus de Teatinos, 29071 Malaga, Spain

3D Semantic Mapping Segmentation Uncertainty Estimation 3D Instance Segmentation 3D Scene Understanding