Embodied Multimodal Grounding for Open-Vocabulary Mobile Manipulation via Semantic 3D Gaussian Splatting

Embodied mobile manipulation requires language, visual observations, three-dimensional scene structure, and action feasibility to be aligned before execution. We study open-vocabulary target grounding with few-shot manipulation in local household workspaces and present an embodied multimodal grounding framework that integrates active multi-view Semantic 3D Gaussian Splatting (Semantic-3DGS), reachability-aware base positioning, and a diffusion-based vision-language-action policy. A task-driven local Semantic-3DGS serves as a shared interface across active sensing, language-conditioned 3D localization, obstacle-aware scene reasoning, base preparation, and semantic conditioning of the action model. To preserve pretrained action priors, the 3D semantic cues are injected only into the late action-expert blocks. In expanded 50-trial real-robot evaluations against representative vision-language-action (VLA) approaches, the full system achieves 60% long-horizon success compared with 40% for PointVLA and 28% for DexVLA, and reaches 74% success in heavily cluttered manipulation compared with 52% for the single-view variant and 46% for PointVLA. It also maintains 75% success under a 75 cm height shift and eliminates photo-induced false grasps. These results indicate that explicit, refreshable 3D semantic grounding can improve robustness under clutter, occlusion, viewpoint variation, and embodiment constraints.

A Light-Weight LiDAR-Inertial SLAM System with Loop Closing

In this work, we propose a lightweight integrated LiDAR-Inertial SLAM system with high efficiency and a great loop closure capacity. We found that the current State-of-the-art LiDAR-Inertial SLAM system has poor performance in loop closure. The LiDAR-Inertial SLAM system often fails with the large drifting and suffers from limited efficiency when faced with large-scale circumstances. In this work, firstly, to improve the speed of the whole LiDAR-Inertial SLAM system, we have proposed a new data structure of the sparse voxel-hashing to enhance the efficiency of the LiDAR-Inertial SLAM system. Secondly, to improve the point cloud-based localization performance, we have integrated the loop closure algorithms to improve the localization performance. Extensive experiments on the real-scene large-scale complicated circumstances demonstrate the great effectiveness and robustness of the proposed LiDAR-Inertial SLAM system.