Autonomous Robotic Mapping of Fragile Geologic Features

TL;DR

This paper introduces a UAV-based system for detecting, localizing, and precisely mapping fragile geologic features like precariously balanced rocks, using deep learning, real-time tracking, and SLAM to improve scientific understanding of landscape stability.

Contribution

The paper presents a novel target-oriented mapping pipeline combining neural detection, real-time tracking, sampling-based localization, and UAV path planning for fragile geological features.

Findings

Effective detection and localization of PBRs from aerial images.

Robust mapping technique resilient to false positives and missed detections.

Enhanced mapping efficiency by focusing on specific targets.

Abstract

Robotic mapping is useful in scientific applications that involve surveying unstructured environments. This paper presents a target-oriented mapping system for sparsely distributed geologic surface features, such as precariously balanced rocks (PBRs), whose geometric fragility parameters can provide valuable information on earthquake shaking history and landscape development for a region. With this geomorphology problem as the test domain, we demonstrate a pipeline for detecting, localizing, and precisely mapping fragile geologic features distributed on a landscape. To do so, we first carry out a lawn-mower search pattern in the survey region from a high elevation using an Unpiloted Aerial Vehicle (UAV). Once a potential PBR target is detected by a deep neural network, we track the bounding box in the image frames using a real-time tracking algorithm. The location and occupancy of the target in world coordinates are estimated using a sampling-based filtering algorithm, where a set of 3D points are re-sampled after weighting by the tracked bounding boxes from different camera perspectives. The converged 3D points provide a prior on 3D bounding shape of a target, which is used for UAV path planning to closely and completely map the target with Simultaneous Localization and Mapping (SLAM). After target mapping, the UAV resumes the lawn-mower search pattern to find the next target. We introduce techniques to make the target mapping robust to false positive and missing detection from the neural network. Our target-oriented mapping system has the advantages of reducing map storage and emphasizing complete visible surface features on specified targets.