#Semantic mapping (7)

OpenBelief-Nav: Evidence-Preserving Object Memory for Open-Vocabulary Language-Guided Navigation

Open-vocabulary 3D scene graphs provide compact semantic memory for language-guided navigation, but mapped objects are often exposed through a single fused feature or committed semantic label. Such commitment can remove minority yet task-relevant hypotheses from the task-time interface. We present OpenBelief-Nav, an evidence-preserving object memory that retains observation-level phrases, reliability cues, and frame-mask provenance while maintaining separate aggregate geometric and visual representations. Semantically related phrases are consolidated into a vocabulary-independent object belief from which task-specific readouts perform fixed-vocabulary projection or free-form retrieval. On five ScanNet200 and eight Replica scenes, full-belief projection achieves mIoU scores of 0.2742 and 0.2912, compared with 0.2393 and 0.2701 for a matched early-commit readout. Across 78 HM3D-YCB navigation trials, consensus and early-commit retrieval each achieve 60/78 successes, compared with 58/78 for belief-weighted retrieval and 55/78 for DualMap. Across 20 Unitree G1 runs organized as 10 matched evaluation cases, a correction policy permitting at most two verified candidate attempts improves target-confirmation success from 6/10 to 8/10 relative to top-1-only execution. Code will be released upon acceptance at https://openbelief-nav.github.io/.

Dinh Tuan Nguyen, Anh Dao, Phuong Nam Dang · 191 views · Aug 14, 2026

Open-Vocabulary Navigation3D scene graphs

Aug 14, 2026

FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction

Existing occupancy prediction relies on large-scale voxel annotations and generalizes poorly across environments, collapsing to near-zero per-class scores on unseen scenes. FreeOcc is the first training-free framework for open-vocabulary occupancy prediction: a SLAM backbone supplies poses and sparse geometry, geometrically anchored Gaussian updates build a dense 3DGS map (ray-aligned anisotropic initialization with s_parallel = gamma * s_perp and Gaussian centers fixed to SLAM points), an off-the-shelf vision-language model attaches open-vocabulary semantics to Gaussian primitives, and a probabilistic Gaussian-to-occupancy projection yields a voxel field queryable online, with no learnable parameter serving the occupancy task. On EmbodiedOcc-ScanNet it reaches 31.29/13.86 monocular and 34.40/15.84 RGB-D IoU/mIoU, more than double the self-supervised baselines; geometric IoU improves 76.1% over the next-best DROID-Splat. The paper also introduces ReplicaOcc, where learned baselines collapse to 0.00-0.01 while FreeOcc transfers zero-shot to 55.65/20.90 and runs online at 25 FPS on a live RealSense D435i stream.

Jiang, Zeyu, Zhou, Changqing, Zuo, Xingxing · 935 views · Apr 30, 2026

3D Gaussian SplattingOccupancy Prediction

Apr 30, 2026

SSTG-Nav: Metric-Grounded Spatial-Semantic Topological Graphs for Reusable Object Navigation

Service robots that live for months in the same homes and facilities should get more reliable with experience instead of re-exploring familiar space for every request. SSTG-Nav turns a one-time, goal-independent survey into a persistent metric-semantic topology: farthest-point sampling builds the graph (6,642 nodes / 21,845 edges, 0.8 m cover radius), view-local VLM detections are depth-back-projected into reachable 0.8 m standoff stopping poses, and source-aware noisy-OR fusion consolidates evidence across viewpoints while retaining residual standoffs for recovery. On 1,000 HM3D-v2 episodes the goal-independent topology reaches a 99.4% geometric success ceiling; holding semantic responses fixed, metric grounding raises SR/SPL from 0.835/0.560 to 0.920/0.603, fusion reaches 0.926/0.586, and fusion-aware Top-3 recovery reaches 0.975/0.601, with a ROS 2/Nav2 realization closing the query-to-execution loop on a physical robot.

Peng, Daojie, Wang, Bingtao, Ma, Jun · 793 views · Aug 1, 2026

Object NavigationSemantic mapping

Aug 1, 2026

DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability

Recent advances in 3D Gaussian Splatting (3DGS) have enabled significant progress in dense dynamic Simultaneous Localization And Mapping (SLAM). Prevailing methods typically discard predefined dynamic objects, ignoring that transiently static objects offer valuable geometric constraints for pose estimation. A recent work attempts to leverage this potential by employing per-pixel uncertainty maps to quantify the magnitude of motion. While this approach enables transiently static objects to enhance pose estimation, it erroneously integrates these objects into the static map, resulting in persistent artifacts. Moreover, its reliance on purely geometric information leads to ambiguous object boundaries in the uncertainty maps. To overcome these limitations, we present DL-SLAM, a monocular Gaussian Splatting SLAM system built upon a novel dual-level probabilistic framework. Our method computes dynamic probability maps by combining semantic and geometric information. These pixel-level probabilities are lifted to 3D and aggregated to derive an object-level dynamic probability for each instance. Object-level probability enables the categorical pruning of dynamic Gaussians, resulting in an artifact-free static map. The static map, in turn, provides a geometrically consistent guidance to refine the pixel-wise probabilities, enhancing their reliability. Experimental results demonstrate that DL-SLAM outperforms existing approaches, improving tracking accuracy by up to 13% while generating high-fidelity semantic maps.

Ziheng Xu, Qingfeng Li, Xuefeng Liu · 797 views · Jul 2, 2026

Dynamic EnvironmentSLAM

Jul 2, 2026

DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

DeWorldSG generates spatio-temporally robust 3D semantic scene graphs from RGB-D sequences. It estimates instance-level geometric 3D Gaussian distributions through depth-guided filtering and represents each object as a probabilistic 3D node. It aggregates spatiotemporal evidence across object pairs and refines relations using contextual priors from a world model (V-JEPA 2). Improves triplet recall by 77.4% and predicate recall by 23.2% over prior SoTA.

Seok-Young Kim, Abdelrahman Elskhawy, Taewook Ha · 541 views · Jul 1, 2026

Scene graphsRGB-D

Jul 1, 2026

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DeWorldSG: depth-aware 3D scene graph generation

@rsasaki0109 · 580 views · 2026-08-13

Scene graphsDeWorldSG

Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair Climbing

We present a novel algorithm for real-time planar semantic mapping tailored for humanoid robots navigating complex terrains such as staircases. Our method is adaptable to any odometry input and leverages GPU-accelerated processes for planar extraction, enabling the rapid generation of globally consistent semantic maps. We utilize an anisotropic diffusion filter on depth images to effectively minimize noise from gradient jumps while preserving essential edge details, enhancing normal vector images' accuracy and smoothness. Both the anisotropic diffusion and the RANSAC-based plane extraction processes are optimized for parallel processing on GPUs, significantly enhancing computational efficiency. Our approach achieves real-time performance, processing single frames at rates exceeding $30 Hz$, which facilitates detailed plane extraction and map management swiftly and efficiently. Extensive testing underscores the algorithm's capabilities in real-time scenarios and demonstrates its practical application in humanoid robot gait planning, significantly improving its ability to navigate dynamic environments.

Teng Bin, Jianming Yao, Tin Lun Lam · 458 views · Nov 4, 2024

HumanoidSemantic mapping

Nov 4, 2024

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