Multiple LiDAR

Multiple LiDAR systems are increasingly used in robotics and autonomous driving to enhance perception and robustness compared to single-LiDAR setups. Current research focuses on improving calibration techniques, often employing neural networks and differentiable rendering to automate the process and reduce reliance on expensive, controlled environments. These advancements are crucial for accurate sensor fusion and improved performance in applications like simultaneous localization and mapping (SLAM), object detection, and odometry, ultimately leading to safer and more reliable autonomous systems. Furthermore, research explores efficient data processing methods for handling the large amounts of data generated by multiple LiDARs, including novel point cloud representations and data augmentation strategies to improve generalization across different sensor configurations.