In the autonomous vehicles field, localization is a crucial aspect. While the ORB-SLAM algorithm is a recognized solution for these tasks, it poses challenges due to its computational intensity. Although accelerated implementation exists, a bottleneck persists in the Point Filtering phase which relies on the Distribute Octree algorithm that is not suitable for GPU processing. In this paper, we introduce a novel GPU-suitable algorithm designed to enhance the Point Filtering step, surpassing Distribute Octree. We conducted a comprehensive comparison with state-of-the-art CPU and GPU implementations, considering both computational time and trajectory accuracy. Our experimental results, demonstrate significant speed-ups up to 3x compared to previous contributions.
High-Performance Feature Extraction for GPU -Accelerated ORB-SLAMx / Muzzini, F.; Capodieci, N.; Cavicchioli, R.; Rouxel, B.. - (2024). (Intervento presentato al convegno 27th Design, Automation and Test in Europe Conference and Exhibition (DATE) tenutosi a Valencia, esp nel MAR 25-27, 2024).
High-Performance Feature Extraction for GPU -Accelerated ORB-SLAMx
Muzzini F.
;Capodieci N.;Cavicchioli R.;Rouxel B.
2024
Abstract
In the autonomous vehicles field, localization is a crucial aspect. While the ORB-SLAM algorithm is a recognized solution for these tasks, it poses challenges due to its computational intensity. Although accelerated implementation exists, a bottleneck persists in the Point Filtering phase which relies on the Distribute Octree algorithm that is not suitable for GPU processing. In this paper, we introduce a novel GPU-suitable algorithm designed to enhance the Point Filtering step, surpassing Distribute Octree. We conducted a comprehensive comparison with state-of-the-art CPU and GPU implementations, considering both computational time and trajectory accuracy. Our experimental results, demonstrate significant speed-ups up to 3x compared to previous contributions.Pubblicazioni consigliate
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