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

TL;DR

MapForest is a versatile robotic system that integrates multi-modal sensors and advanced mapping algorithms to efficiently detect and map invasive tree species in large, challenging forest environments.

Contribution

The paper introduces MapForest, a modular, platform-agnostic system combining LiDAR, imagery, and robust mapping techniques for invasive species localization in forests.

Findings

Achieved 1.95 m trajectory deviation over 1.2 km in forests.

Detected Tree-of-Heaven with an F1 score of 0.653.

Demonstrated system effectiveness across diverse environments.

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.