Gallopwave focuses on the development of sensor and map fusion technologies, leveraging redundancy across multiple sensors to compensate for performance degradation of individual sensors in challenging scenarios, thereby delivering the most reliable positioning and measurement results.
Applications: Automotive and UAV
Gallopwave applies deep learning techniques to recognize environmental objects, including static objects and map elements required for positioning systems, as well as dynamic objects used for vehicle decision-making and control. The system also predicts object trajectories to support advanced perception and navigation. By leveraging heterogeneous multi-core computing architectures, real-time environmental perception is achieved on low-power embedded platforms.
Applications: Automotive and UAV
Through tightly-coupled fusion of visual, IMU, and GNSS data, Gallopwave leverages visual feature points to detect and compensate for multipath interference in satellite signals, achieving lane-level positioning accuracy. This approach addresses positioning challenges in urban environments and areas lacking high-precision map coverage.
Applications: Automotive and UAV
By performing signal verification and multi-source data fusion, Gallopwave prevents spoofed GNSS signals and malicious interference, ensuring the authenticity and security of positioning results.
Applications: UAV
Gallopwaves Target Acquire System provides efficient, automated detection and tracking of multiple targets, supporting real-time localization, identification, and tracking to enhance operational precision.
Applications: UAV