Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
This addresses the problem of enabling UAVs to operate autonomously in challenging, unstructured environments like forests, which is incremental as it builds on existing semantic SLAM and planning methods.
The authors tackled autonomous flight and mapping in dense, GPS-denied forest environments by developing an integrated system that performs real-time semantic SLAM using LiDAR to detect tree trunks and ground planes, achieving accurate and safe large-scale missions with drift compensation.
Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments. In this letter, we propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. The autonomous navigation module utilizes a multi-level planning and mapping framework and computes dynamically feasible trajectories that lead the UAV to build a semantic map of the user-defined region of interest in a computationally and storage efficient manner. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability. This leads the UAV to execute its mission accurately and safely at scale.