| Autor: |
K. Soroush Sheikhpour, Mohamed Atia |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 10, Pp 86384-86394 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3199384 |
| Popis: |
In autonomous navigation technologies, the Multi-State Constraint Kalman Filter (MSCKF) is one of the most accurate and robust tightly-coupled fusion frameworks for Visual-Inertial Navigation (VIN). However, the adoption of the MSCKF VIN system in real-time embedded applications depends heavily on an efficient implementation of its tangled pipeline. This work initially proposes a novel parallel multi-thread implementation of the MSCKF VIN pipeline on an embedded CPU-enabled hardware that has speeded up the per-epoch processing time of the pipeline by 41% compared to the conventional sequential implementation. The heart of the MSCKF pipeline’s visual backend is an inertially-aided 3D localization of visual feature points that are reduced to a set of nonlinear optimization problems which were conventionally solved in a serial fashion using the single-objective Gauss-Newton optimization algorithm. This work leveraged the parallel architecture of an embedded GPU and further proposes an efficient parallel implementation of a multi-objective Gauss-Newton algorithm. Integration of the proposed GPU-accelerated feature localization technique in the MSCKF parallel pipeline has resulted in 33% faster per-epoch processing time and consequently, the satisfaction of strict real-time constraints. The proposed parallel MSCKF VIN pipelines have been developed using C++ and CUDA on the NVIDIA Jetson TX2 embedded board. Experimental evaluations on a real visual-inertial odometry dataset have been provided to validate the efficacy and real-time performance enhancement of the proposed parallel implementation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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