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Article Dans Une Revue Robotics and Autonomous Systems Année : 2024

Comparing Metrics for Evaluating 3D Map Quality in Natural Environments

Marianne Clausel
Cedric Pradalier

Résumé

In this study, we focus on addressing the challenge of measuring the 3D-map quality in natural environments. Specifically, we consider scenarios where the map is built using a robot’s 3D-Lidar point cloud observations, with potential uncertainty in the robot localization. When considering a natural environment, such as a park or a forest, unstructured by nature, another difficulty arises: the data becomes extremely sparse. As a result, measuring the map quality becomes even more challenging. This study aims to compare the effectiveness of various metrics in measuring the 3D-map quality. Firstly, we evaluate these metrics in a controlled experimental setup, where the reconstructed map is created by progressively degrading the reference map using different degradation models. Secondly, we compare their ability to measure 3D-map quality at a local level, across various simulated environments, ranging from structured to unstructured. Finally, we conduct a qualitative comparison to demonstrate the robustness of certain metrics to noise in the robot localization. This qualitative comparison is done both in simulation and in a real world experiment. Ultimately, we synthesize the properties of these metrics and provide practical recommendations for their selection.
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Dates et versions

hal-04128242 , version 1 (14-06-2023)
hal-04128242 , version 2 (08-01-2024)

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Stéphanie Aravecchia, Marianne Clausel, Cedric Pradalier. Comparing Metrics for Evaluating 3D Map Quality in Natural Environments. Robotics and Autonomous Systems, 2024, 173, ⟨10.1016/j.robot.2023.104617⟩. ⟨hal-04128242v2⟩
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