This paper presents a semantic mapping system that integrates Vision-Language Models (VLMs) with Simultaneous Localization and Mapping (SLAM) to enhance the understanding of indoor environments. While traditional SLAM systems primarily focus on geometric occupancy, they often lack the semantic context needed for high-level robotic tasks. The proposed framework bifurcates the mapping process into two synchronized subsystems: Geometric SLAM and VLM Recognition. The first system uses LiDAR and odometry data to perform real-time localization and mapping, providing a stable and accurate environment for semantic data integration. Simultaneously, the second system leverages a VLM with a ZED2 depth camera to identify furniture and perform precise object size estimation, mapping these semantic entities into the global coordinate frame. A data fusion layer then synthesizes these inputs into a comprehensive Semantic Map that includes both geometric structures and physical object dimensions. Experimental results demonstrate that our integrated approach achieves precise spatial anchoring of semantic entities and improves overall mapping stability, providing a robust foundation for robot environmental understanding in complex indoor scenarios.
LLM-based multi-agent systems (MAS) have exhibited remarkable capabilities in collaborative reasoning and decision-making, yet their interconnected communications introduce new systemic risk: localized hallucinations can propagate along agent communication chain, amplify through interactions, and ultimately trigger cascading failures. Existing countermeasures predominantly follow a post-hoc paradigm, identifying failures only after unsafe behaviors emerge, by which time harmful effects may have already spread throughout the agent network. To tackle this problem, we investigate a complementary pre-hoc approach and propose HalluProp, a Propagation-aware Hallucination inference framework that estimates individual agent failures and emergent system-level hallucination risks before inter-agent interaction. First, we model intrinsic hallucination risks by identifying fine-grained semantic misalignment between agent roles and task queries. We then characterize inter-agent risk propagation by modeling both semantic influence and communication topology. Finally, we integrate these two risks via a differentiable Noisy-OR inference mechanism to derive a systemic diagnosis. Extensive experiments show that HalluProp accurately localizes faulty agents, achieving an average AUROC of 84.6%, while enabling sub-second diagnosis with over $65\times$ speedup over post-hoc methods. By facilitating early intervention through upstream screening, HalluProp effectively complements post-hoc methods, highlighting the potential of pre-hoc risk inference for building more reliable multi-agent systems.
Shi Lin, Chenpei Wang, Peng Qian et al.· 0 citations