Localization and Mapping of Sparse Geologic Features with Unpiloted Aircraft Systems

TL;DR

This paper introduces a UAV-based system for real-time localization and mapping of sparse, fragile geologic features like PBRs, combining deep learning, Kalman filtering, and RGBD SLAM for environmental surveys.

Contribution

It presents a novel integrated system that detects, tracks, and maps sparse geological features using UAVs with real-time deep learning and advanced filtering techniques.

Findings

Successful real-time detection and tracking of PBRs.

Accurate 3D localization of sparse features.

Effective UAV path planning for feature mapping.

Abstract

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.