Mapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention
This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting...
| Autores: | ; ; ; ; ; |
|---|---|
| Tipo de documento: | Artículo |
| Estado: | Published version |
| Fecha de publicación: | 2023 |
| País: | Chile |
| Institución: | Pontificia Universidad Católica de Chile |
| Repositorio: | Pontificia Universidad Católica de Chile |
| OAI Identifier: | oai:repositorio.uc.cl:11534/75006 |
| Acceso en línea: | https://doi.org/10.3390/f14081601 https://repositorio.uc.cl/handle/11534/75006 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | Autonomous drones Flammable material detection Forest inventory Forest monitoring LiDAR Remote sensing Semantic mapping Simultaneous localization and mapping (SLAM) Vegetation classification 15 Life on land 09 Industry, innovation and infrastructure 15 Vida de ecosistemas terrestres 09 Industria, innovación e infraestructura |
| Sumario: | This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system’s capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management. |
|---|