Sparse Point-Cloud Semantic Understanding for Autonomous Mobile Sensing with Cross-Sensor Scene-Graph Reasoning in Complex Environments
DOI:
https://doi.org/10.66857/jirt.v3i2.50Keywords:
Sparse Point Clouds, Scene Graph Reasoning, Cross-Sensor Fusion, Autonomous Mobile Sensing, Semantic UnderstandingAbstract
The rapid advancement of autonomous mobile sensing heavily relies on the ability of robotic systems to perceive and comprehend complex, dynamic environments. Among the various sensing modalities, Light Detection and Ranging provides critical three-dimensional structural information, yet it inherently suffers from spatial sparsity, particularly at extended ranges. This paper introduces a comprehensive framework for sparse point-cloud semantic understanding, augmented by cross-sensor scene-graph reasoning, designed specifically for autonomous mobile platforms operating in intricate real-world conditions. By integrating high-resolution visual data from cameras with sparse geometric data from laser scanners, the proposed methodology bridges the semantic gap that traditionally limits unimodal perception systems. We propose a novel architecture that extracts fine-grained local geometric features from sparse point clouds and fuses them with dense visual embeddings using a multi-modal attention mechanism. Furthermore, we construct a dynamic three-dimensional scene graph where nodes represent semantic entities and edges encapsulate their spatiotemporal relationships. This graph-based reasoning allows the system to infer complex interactions, such as occlusion dependencies and trajectory forecasting, which are crucial for safe navigation. Extensive evaluations on standard autonomous driving datasets demonstrate the superior performance of our approach in semantic segmentation and relational reasoning tasks compared to existing state-of-the-art methods. The integration of structural topology through scene graphs significantly enhances the robustness of semantic understanding in adverse weather and low-lighting conditions, paving the way for more resilient autonomous mobile sensing systems.
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