MapForest: A Modular Field Robotics System for Forest Mapping and Invasive Species Localization

2026-03-23

AI Summary

This study addresses the challenges of monitoring invasive tree species in large, forested areas, where limited accessibility, inefficient manual surveys, and degraded GNSS signals under canopy hinder effective surveillance. The authors propose a lightweight, platform-agnostic robotic system that integrates LiDAR-inertial SLAM with covariance-aware GNSS factors and robust optimization to achieve reliable mapping under intermittent GNSS conditions. By fusing RGB-based object detection—specifically targeting Ailanthus altissima—with georegistration, the system generates actionable invasive species distribution maps for decision support. The framework is deployable on diverse mobile platforms, including drones, bicycles, and backpacks. Evaluated over a 1.2-kilometer forest trail, the system achieved a trajectory error of only 1.95 meters and an F1 score of 0.653 for Ailanthus detection. Additionally, the authors release a dataset from six field sites to support reproducible research.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Remote Sensing / Geospatial AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Monitoring and controlling invasive tree species across large forests, parks, and trail networks is challenging due to limited accessibility, reliance on manual scouting, and degraded under-canopy GNSS. We present MapForest, a modular field robotics system that transforms multi-modal sensor data into GIS-ready invasive-species maps. Our system features: (i) a compact, platform-agnostic sensing payload that can be rapidly mounted on UAV, bicycle, or backpack platforms, and (ii) a software pipeline comprising LiDAR-inertial mapping, image-based invasive-species detection, and georeferenced map generation. To ensure reliable operation in GNSS-intermittent environments, we enhance a LiDAR-inertial mapping backbone with covariance-aware GNSS factors and robust loss kernels. We train an object detector to detect the Tree-of-Heaven (Ailanthus altissima) from onboard RGB imagery and fuse detections with the reconstructed map to produce geospatial outputs suitable for downstream decision making. We collected a dataset spanning six sites across urban environments, parks, trails, and forests to evaluate individual system modules, and report end-to-end results on two sites containing Tree-of-Heaven. The enhanced mapping module achieved a trajectory deviation error of 1.95 m over a 1.2 km forest traversal, and the Tree-of-Heaven detector achieved an F1 score of 0.653. The datasets and associated tooling are released to support reproducible research in forest mapping and invasive-species monitoring.

Problem

• invasive species
• forest mapping
• GNSS-denied environments
• field robotics
• Tree-of-Heaven

Innovation

• modular robotics
• LiDAR-inertial mapping
• invasive species detection
• GNSS-denied navigation
• georeferenced mapping