Localization and Mapping of Sparse Geologic Features with Unpiloted Aircraft Systems
This addresses the need for efficient environmental surveys in geomorphology, though it is incremental as it combines existing methods like deep learning and SLAM for a specific application.
The paper tackles the problem of autonomously locating and mapping sparsely distributed geologic features like precariously balanced rocks using an unpiloted aircraft system, achieving real-time detection with a deep neural network and precise 3D localization through depth filtering and SLAM.
Robotic mapping is attractive in many scientific applications that involve environmental surveys. This paper presents a system for localization and mapping of sparsely distributed surface features such as precariously balanced rocks (PBRs), whose geometric fragility parameters provide valuable information on earthquake processes and landscape development. With this geomorphologic problem as the test domain, we carry out a lawn-mower search pattern from a high elevation using an Unpiloted Aerial Vehicle (UAV) equipped with a flight controller, GPS module, stereo camera, and onboard computer. Once a potential PBR target is detected by a deep neural network in real time, we track its bounding box in the image coordinates by applying a Kalman filter that fuses the deep learning detection with Kanade-Lucas-Tomasi (KLT) tracking. The target is localized in world coordinates using depth filtering where a set of 3D points are filtered by object bounding boxes from different camera perspectives. The 3D points also provide a strong prior on target shape, which is used for UAV path planning to closely map the target using RGBD SLAM. After target mapping, the UAS resumes the lawn-mower search pattern to locate and map the next target.