This document discusses enhancing forest navigation and mapping with AI-controlled ground robots to overcome challenges of uneven terrain in digital forestry. It summarizes the development of a 3D simultaneous localization and mapping (SLAM) system for an Archimede rover to enable autonomous navigation and data collection across forest settings. Experimental results demonstrate reconstruction of 3D point clouds and maps from simulation and real-world testing, though challenging scenarios revealed limitations with sensors that could be addressed through additional sensors or data fusion. The overall goal is to train robot controllers for forest navigation using simulation environments modeled from collected data to generate digital twins of forests.

1. Enhancing Forest Navigation and Mapping
with AI-Controlled Ground Mobile Robots:
Overcoming Challenges of Uneven Terrain
in Digital Forestry
Candidate:
Marco Giberna
Space Robotics Lab
Supervisor:
Dr. Stefano Seriani
Co-Supervisor:
Dr. Matteo Caruso
Academic Year 2022/2023
Computer and Electronic Engineering
Robotics and Artificial Intelligence

2. INTRODUCTION
Picture of the burnt Italian and Slovenian Karst, from TriestePrima, 2022

3. INTRODUCTION
¡ Monitoring and surveillance of forest environments play a
crucial role in conserving ecosystems and in fighting climate
change

4. INTRODUCTION
¡ Digital Twin: virtual representation of a physical system
¡ Forest Digital Twin: multi-layer representation
¡ Terrain Geometry
¡ Terrain Texture
¡ Terrain Properties
¡ Tree/Plants Segmentation
¡ Tree/Plants Additional Data (e.g. species, height, diameter, vitality, …)

5. INTRODUCTION
Image from Song, J. et al. "A Method for Quantifying Understory Leaf Area Index in a Temperate Forest
through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data" Remote Sensing, 2021, 13
¡ Forest’s Terrain:
¡ Strongly uneven
¡ Heterogenous
¡ Deformable

6. INTRODUCTION
Data Acquisition Simulation
Environment Creation
Controller Training in the
Simulation Environment
Deployment on
Physical Platform
TERRAIN GEOMETRY +
TEXTURE
TERRAIN DRIVING
CHARACTERISTICS
SCANNING
(IMAGERY
ACQUISITION)
DRIVING AND
MEASURING SIMULATION
ENVIRONMENT
TREE DETECTION +
TRAVERSABILITY ANALYSIS +
PATH PLANNING

7. INTRODUCTION
¡ Traversability: robot’s ability to traverse a given patch of
ground, considering:
¡ Travel Time
¡ Energy Consumption
¡ Risk of Getting Stuck
¡ Risk of Falling
¡ Slippage (difference between perceived and actual position)
¡ …

8. ARCHIMEDE ROVER
Archimede Rover in action

9. ARCHIMEDE ROVER
Flexible Legs
Steerable Wheels
Battery
Packs
Intel
RealSense
Depth
Camera
2D LiDAR Control
Board
On-Board
Computer

10. SIMULATION
Digital Twin of Archimede Rover in Gazebo

11. ROBOTIC OPERATING SYSTEM

12. ROBOTIC OPERATING SYSTEM – ROS

13. SIMULTANEOUS LOCALIZATION AND MAPPING

14. SLAM

15. SIMULTANEOUS LOCALIZATION AND MAPPING
¡ Cartographer is an open-source SLAM system
¡ Provides a ROS integration
¡ Supports 2D and 3D map generation
¡ Supports multiple sensors
¡ Requires fine tuning

16. EXPERIMENTAL SETUP
Archimede Rover during a test at DLR

17. EXPERIMENTAL SETUP – SIMULATION
Simulation World #1 - Flat Simulation World #2 - Slope

18. EXPERIMENTAL SETUP – REAL WORLD
Corridor
(University of Trieste)
Bothanical Garden
(University of Trieste)
Path

19. EXPERIMENTAL SETUP – REAL WORLD
Outdoor Planetary
Exploration Testbed
(DLR, Institute of
Robotics and
Mechatronics, Munich)

20. RESULTS
3D reconstruction of a simulation world

21. RESULTS – SIMULATION WORLD
3D Pointcloud Reconstruction
Orthogonal
Projections
of the 3D
Map

22. RESULTS – REAL WORLD – CORRIDOR
3D Pointcloud Reconstruction
Orthogonal Projection of
the 3D Map

23. RESULTS – REAL WORLD – GARDEN
3D Pointcloud Reconstruction
Orthogonal Projection of
the 3D Map

24. RESULTS – REAL WORLD – DLR I
3D Pointcloud Reconstruction
Orthogonal Projections of
the 3D Map

25. RESULTS – REAL WORLD – DLR II
3D Pointcloud Reconstruction
Picture of the Travelled Zone
with correspondences

26. DISCUSSION
Archimede Rover in action

27. DISCUSSION
¡ Poor results in challenging scenario because of:
¡ Low structurality of the environment
¡ Limited Field of View and vision sensors only looking forward
¡ Low quality camera’s acquistions
¡ Weight imbalance and large vibrations affecting IMUs

28. DISCUSSION
¡ Possible Solutions:
¡ Additional 3D LIDAR or tilted 2D LIDAR facing backwards
¡ Fusing the available IMUs to improve data quality and to get
another odometry source

29. CONCLUSION
A Thoughtful Archimede Rover

30. CONCLUSION
¡ Development of 3D SLAM system for the Archimede rover
¡ Implementation of an autonomous navigation system
¡ Datasets collection across various settings, enabling 3D
reconstruction for creating the simulation environment
designed for training mobile robot controllers for forest
navigation and generation of their Digital Twins
¡ Analysis of the performance of the SLAM system