ALG-TECH’s Vision-Radar Multi-Modal Perception System integrates stereo vision’s spatial reconstruction with radar’s penetration advantages to create real-time multi-environment perception. Leveraging deep learning algorithms, neural networks execute high-accuracy visual relocalization, obstacle detection, and mapping functions, achieving autonomous positioning without GPS dependency—ideal for smart rail transit and unmanned mobile platforms.
The Vision-Radar Multi-Modal Perception System integrates multi-source data, achieves precise perception and positioning through deep learning algorithms, and provides high-precision and high-reliability positioning solutions for rail transit.
Integrates binocular stereo-depth vision with radar precision ranging to achieve millisecond-level spatiotemporal data synchronization, enabling multidimensional environmental perception and high-precision positioning.
Leveraging the robust computational capabilities of X86 architecture and NVIDIA high-performance GPUs to process sensor data in real time, enhancing navigation responsiveness and decision-making accuracy.
Optimizes stereo vision matching accuracy through deep neural networks, integrating machine learning algorithms for intelligent obstacle prediction and visual relocalization. This significantly enhances computational precision in complex scenarios.
Features a dual-redundant architecture design validated through -40℃ to 85℃ wide-temperature testing, ensuring high reliability and stability in extreme environmental conditions.
