Towards Intelligent LiDAR with Adaptive Focus

As the adoption of LiDAR expands across various fields such as autonomous driving, robotics, and smart cities, the demand for adaptive scanning capabilities to better capture dynamic and complex scenes becomes paramount. Current LiDAR technologies, limited by fixed uniform scan patterns, struggle to prioritize critical areas, resulting in reduced perception accuracy and performance inefficiencies. This paper introduces SmartLiDAR, an advanced LiDAR system that enhances scanning efficiency and performance by adaptively optimizing scan focus through an intelligent, software-defined micro-mirror controller. Unlike traditional systems, SmartLiDAR dynamically adjusts its scan pattern based on environmental characteristics and application-specific requirements, concentrating sample points on key objects without increasing power consumption or scan time. SmartLiDAR achieves this by integrating a novel quadratic micro-mirror controller, an adaptive algorithm for generating fine-grained attention map with prioritized scan focus, and a carefully designed optimization algorithm that maps attention maps to practical scanning patterns. We prototype SmartLiDAR by building a software-defined LiDAR using commercially available optical components and FPGA. Our experimental results demonstrate that SmartLiDAR significantly enhances resolution in regions of interest by 3x and increases average object detection precision by up to 16.11%. Additionally, SmartLiDAR maintains negligible extra energy consumption and processing latency, making it suitable for real-time applications, such as autonomous vehicles.