Robotics in Forest Inventories: SPOT’s First Steps

Author:

Chirici Gherardo12ORCID,Giannetti Francesca1ORCID,D’Amico Giovanni13ORCID,Vangi Elia14ORCID,Francini Saverio15ORCID,Borghi Costanza12ORCID,Corona Piermaria3ORCID,Travaglini Davide1ORCID

Affiliation:

1. geoLAB—Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Firenze, Italy

2. Fondazione per il Futuro delle Città, 50133 Firenze, Italy

3. Research Centre for Forestry and Wood—CREA Council for Agricultural Research and Economics, Viale Santa Margherita 80, 52100 Arezzo, Italy

4. Italian National Research Council—CNR, Institute for Mediterranean Agriculture and Forest Systems ISAFoM, 06128 Perugia, Italy

5. National Biodiversity Future Center (NBFC), 90133 Palermo, Italy

Abstract

In the context of the potential future use of unmanned ground vehicles for forest inventories, we present the first experiences with SPOT, a legged robot equipped with a LiDAR instrument and several cameras that have been used with a teleoperation approach for single-tree detection and measurements. This first test was carried out using the default LiDAR system (the so-called enhanced autonomy payload-EAP, installed on the board of SPOT to guide autonomous movements) to understand advantages and limitations of this platform to support forest inventory activities. The test was carried out in the Vallombrosa forest (Italy) by assessing different data acquisition methods. The first results showed that EAP LiDAR generated noisy point clouds where only large trees (DBH ≥ 20 cm) could be identified. The results showed that the accuracy in tree identification and DBH measurements were strongly influenced by the path used for data acquisition, with average errors in tree positioning no less than 1.9 m. Despite this, the best methods allowed the correct identification of 97% of large trees.

Funder

University of Florence

Publisher

MDPI AG

Subject

Forestry

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