Vision-Radar Multi-Modal Perception System

ALG-TECH introduces the Vision-Radar Multimodal Perception System, which integrates the spatial modeling capabilities of binocular stereo vision with the high-penetration detection advantages of radar to build real-time multimodal environmental perception capabilities. Powered by a deep learning algorithm engine, it utilizes neural networks to achieve high-precision visual relocalization, obstacle detection, map building, and other functions, enabling autonomous positioning without reliance on GPS. This system is applicable to intelligent rail transit, auxiliary safety, and navigation systems for mobile unmanned vehicles.

All-Environment Reliability

Features a dual-redundant architecture design and has passed rigorous testing across a wide temperature range of -40°C to 85°C, ensuring high reliability and stability in various harsh environments.

AI-Driven Dynamic Navigation

Optimizes stereo vision matching accuracy based on deep neural networks and integrates machine learning algorithms for intelligent obstacle prediction and visual relocalization, significantly improving computational precision in complex scenarios.

High-Efficiency X86 Computing Power

Leverages the strong computational performance of the X86 architecture and NVIDIA high-performance GPUs to process sensor data in real time, enhancing navigation response speed and decision-making accuracy.

Multi-Dimensional Fusion for Synchronized Positioning

Combines binocular stereo depth vision with precise radar ranging, achieving millisecond-level spatiotemporal data synchronization to construct multi-dimensional environmental perception and high-precision positioning.