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Related Experiment Video

Updated: Jul 18, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images.

Lucas A Wells¹, Woodam Chung¹

¹Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, Corvallis, OR 97331, USA.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This study presents an integrated approach for accurate forest inventory, combining camera-based motion detection with Global Navigation Satellite System (GNSS) positioning. This method generates precise, real-time stem maps, improving upon traditional methods for enhanced forest management.

Keywords:

GNSS/GPS computer vision direct visual odometry non-linear least squares simultaneous localization and mapping

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Area of Science:

Forestry Science
Remote Sensing
Geomatics Engineering

Background:

Traditional forest inventories rely on stand-level descriptors, limiting detailed spatial analysis.
Accurate individual tree mapping is crucial for understanding forest ecosystems and optimizing silvicultural practices.
Existing aerial and terrestrial methods have limitations in directly estimating dendrometric variables or are constrained by Global Navigation Satellite System (GNSS) accuracy.

Purpose of the Study:

To develop and validate an integrated approach for high-resolution individual tree mapping.
To enhance the accuracy and real-time generation of forest stem maps.
To improve site-specific silvicultural prescriptions through detailed spatial and dendrometric data.

Main Methods:

Integration of a camera-based motion and tree detection system with GNSS positioning.
Development of a visual egomotion estimation algorithm to improve GNSS positioning consistency.
Application of a least squares minimization technique for optimizing pose tracks and tree stem positions.

Main Results:

Generation of large-scale stem maps in real time with a root mean squared position error of 2.16 m.
Achieved twice the accuracy of using a consumer-grade GNSS receiver alone.
Demonstrated the effectiveness of the integrated approach for detailed forest spatial structure analysis.

Conclusions:

The integrated camera-GNSS approach significantly enhances the accuracy of individual tree mapping.
Real-time stem map generation facilitates timely and informed forest management decisions.
This method provides a robust framework for detailed forest inventory and precision silviculture.