AI-Driven Landing Zone Detection Module for Vertical Take-Off and Landing Vehicles Using Projection-Based LiDAR-Navigation Pipelines

This paper introduces an artificial intelligence-based landing zone detection module (LZDM) for vertical take-off and landing (VTOL) navigation. It employs a projection-based point cloud semantic segmentation (PCSS) convolutional neural network model combined with point cloud accumulation and a range image generation module. The proposed method addresses the limitations of existing projection-based PCSS methods, which often struggle with low-resolution and non-repetitive scan raw light detection and ranging (LiDAR) data commonly found in aerial datasets. The proposed LZDM was developed using three sets of aerial datasets collected from a DJI M600 hexacopter drone, a DJI M300 RTK quadrotor, and a Bell412 helicopter. The results were evaluated using both qualitative and quantitative metrics, demonstrating its robustness and effectiveness. In terms of quantitative results, the proposed method achieved mean intersection over union and accuracy values greater than 0.93 and 98 percent, respectively, across all three datasets, highlighting its accuracy in identifying safe landing zones (LZs). To assess the real-time feasibility of the proposed LZDM, it was deployed on a reconfigurable hardware-accelerated module. This setup achieved processing rates higher than 10 Hz for all three datasets and a throughput of over 5 million pts/s on the Jetson AGX Xavier dedicated hardware combined with the PyTorch TensorRT optimization module. The supplementary materials, including the inference code, sample testing data, and instructions are available in our GitHub repository at https://github.com/nira16herath/CENet-LZ-detection/tree/main Note to Practitioners—VTOLs, such as small drones and helicopters, are increasingly becoming popular in time-critical and safety-critical operations, including parcel delivery to remote areas, search and rescue, and other autonomous missions. While advanced trajectory planning systems are available to guide these vehicles to designated (or targeted) positions, the system also demands reliable and accurate landing of the vehicle at nominal and emergency situations. Therefore, ensuring reliable and rapid LZ detection to improve safe autonomous landing and expedite decision-making remains a significant challenge, particularly in unknown and unprepared terrains. Autonomous systems must identify safe LZs with precision and should avoid landing on unsafe areas, such as water bodies, inclined surfaces, or other unstable surfaces. Furthermore, VTOL-based aerial datasets typically generate low-resolution and non-repetitive scan LiDAR point clouds, making accurate LZ detection more challenging. The work proposed in the paper provides a fast, accurate, and semantic-aware LiDAR-based LZ detection that provides a low risk of misidentification, which ensures safe landings in complex environments. The proposed method has been trained and validated using real-world custom-labeled aerial datasets and deployed on reconfigurable hardware, ensuring real-time suitability for LZ detection in diverse environments. Since the method relies solely on LiDAR sensors, practitioners can implement the proposed method without additional sensor modalities or high-resolution LiDAR point clouds of environments.