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Slide 1 - S2 Perception Core: A Dual-Pipeline Architecture for Semantic Navigation
The slide highlights the challenge of processing unstructured raw sensor data from LiDAR, cameras, and IMUs, which complicates extracting semantic information for navigation in dynamic, complex environments, where traditional methods falter in real-time accuracy. It introduces the S2 Perception Core's dual-pipeline architecture in ROS2, which integrates Vision-Language Models: a fast path for basic detection and a smart path for advanced reasoning, efficiently converting data into actionable navigation insights.
S2 Perception Core: A Dual-Pipeline Architecture for Semantic Navigation
| The Challenge: Unstructured Data | The Solution: S2 Architecture |
|---|---|
| Raw sensor data from LiDAR, cameras, and IMUs arrives in unstructured formats, making it difficult to extract meaningful semantic information for navigation in dynamic environments. Traditional methods struggle with real-time processing and accuracy in complex, unstructured terrains. | S2 Perception Core employs a dual-pipeline architecture in ROS2, integrating Vision-Language Models (VLMs) for semantic understanding. The Fast path handles basic detection for speed, while the Smart path leverages VLMs for complex reasoning, transforming raw data into actionable insights efficiently. |
Speaker Notes
Subtitle: Transforming Raw Sensor Data into Actionable Insight via ROS2 & VLMs. Include a central triptych diagram: left - raw point cloud data; center - dual-pipeline S2 Core with Fast and Smart paths; right - structured output with occupancy grid and trajectory prediction. Use Unreal Engine 5 visual style, dark UI, cyber-engineering aesthetic. Technical Impact - Metrics: Achieves 95% accuracy in object detection, reduces latency by 60% compared to single-pipeline systems, and enables robust trajectory prediction in unstructured terrains. Integrated with ROS2 for seamless deployment.
