Autonomous Exploration Under Canopy for Forest Investigation Using LiDAR and Quadrotor

Efficiently collecting 3-D information in complex forest environments holds significant importance for forest surveys. Terrestrial laser scanning (TLS) or ground-based mobile laser scanning (MLS) can acquire 3-D forest information in relatively smooth terrain conditions, but they still face the challenge of time-consuming and labor consumption. In comparison, small unmanned aerial vehicle (UAV) measurement systems with efficient autonomous exploration capabilities are becoming a new close-range sensing platform for acquiring forest data within intricate canopy environments due to their high-efficiency maneuverability and flexibility. However, achieving efficient autonomous exploration for UAVs remains a significant challenge in complex forest environments. In this article, we propose a novel autonomous exploration method without relying on the global navigation satellite system. Three heuristic waypoint generation algorithms are introduced to facilitate efficient autonomous exploration of intricate and unknown forest environments using quadrotor. Subsequently, employing nonlinear optimization, B-spline curves are employed for generating smooth, collision-free, and dynamically feasible local planning trajectories. Finally, a sliding window strategy is utilized to quickly adjust the trajectory when obstacles like tree branches are detected. This ensures that the quadrotor can fly without collisions by promptly replanning its path. We have conducted a series of benchmarked experiments within plantation and natural forest environments. Compared with the classic next-best-view planning (NBVP) method, the proposed method can complete exploration 3–7 times faster than NBVP. Compared with the fast UAV exploration (FUEL) and fast autonomous exploration planner (FAEP) methods, this method reduces the average flight time and distance by 60%. Furthermore, we also validated the effectiveness of the proposed methods in real forest experiments.