A Real-Time CPU-GPU Embedded Implementation of a Tightly-Coupled Visual-Inertial Navigation System

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 a...

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Veröffentlicht in:IEEE access Jg. 10; S. 86384 - 86394
Hauptverfasser: Sheikhpour, K. Soroush, Atia, Mohamed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Autonomous navigation
Autonomous systems
Central processing units
CPUs
Embedded software
Embedded systems
Graphics processing units
Inertial navigation
kalman filter
Kalman filters
Localization
Navigation systems
Optimization
Parallel processing
Pipelining (computers)
Real time
Real-time systems
sensor fusion
Sensors
visual-inertial navigation
Visualization
ISSN:2169-3536, 2169-3536
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Zusammenfassung: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3199384