Comparing metrics for evaluating 3D map quality in natural environments

Aravecchia, Stéphanie; Clausel, Marianne; Pradalier, Cédric

doi:10.1016/j.robot.2023.104617

Robotics and Autonomous Systems

2024

DOI: 10.1016/j.robot.2023.104617

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Comparing metrics for evaluating 3D map quality in natural environments

Stéphanie Aravecchia,

Marianne Clausel,

Cédric Pradalier

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2024

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Real-time, dense UAV mapping by leveraging monocular depth prediction with monocular-inertial SLAM

Habib,

Papadakis,

Le Barz

et al. 2024

Advanced Robotics

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We present a dense and metric 3D mapping pipeline designed for embedded operation on-board UAVs, by loosely coupling deep neural networks trained to infer dense depth single images with a SLAM system that restores metric scale from sparse depth. In contrast to computationally restrictive approaches that leverage multiple views, we propose a highly efficient, single-view approach without sacrificing 3D mapping performance. This enables real-time construction of a global 3D voxel map by iterative fusion of the rescaled dense depth maps obtained via raycasting from the estimated camera poses. Quantitative and qualitative experimentations of our framework in challenging environmental conditions show comparable or superior performance with respect to state-of-the-art approaches via a better effectiveness-efficiency trade-off.

show abstract

Real-time, dense UAV mapping by leveraging monocular depth prediction with monocular-inertial SLAM

Habib,

Papadakis,

Le Barz

et al. 2024

Advanced Robotics

View full text Add to dashboard Cite

show abstract

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Comparing metrics for evaluating 3D map quality in natural environments

Cited by 1 publication

References 30 publications

Real-time, dense UAV mapping by leveraging monocular depth prediction with monocular-inertial SLAM

Real-time, dense UAV mapping by leveraging monocular depth prediction with monocular-inertial SLAM

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