OpenCL and OpenGL Implementation of Simultaneous Localization and Mapping Algorithm using High-End GPU

Abstract

Simultaneous Localization And Mapping (SLAM) algorithms are being used in many robotic applications and autonomous navigation systems. The FastSLAM2.0 addresses an issue of the SLAM problem and allows a robot to navigate in an unknown environment. Several works have presented many algorithmic optimizations to reduce the computational complexity of such algorithm. In this paper, a GPGPU (general-purpose computing on graphics processing units) is exploited to achieve a parallel implementation of the FastSLAM2.0. The GPGPU acceleration is done using two different implementations for parallel programming. The first implementation used OpenGL shading language which is based on the characteristics of graphics hardwares. The second implementation used OpenCL which allows hardware acceleration across heterogeneous architectures. We also explored the impact of the two approaches on the the resulting GPGPU implementation. A comparison related to processing-time and localization accuracy is made using a real indoor dataset. Our results show a significant speedup of the GPGPU implementation over a Quad-Core CPU. We show also that, by adopting the same optimization methodology using the two approachs, the OpenCL implementation is faster and suitable for GPGPU accelerated SLAM algorithms.

Accurate Global Point Cloud Registration Using GPU-Based Parallel Angular Radon Spectrum

“…Even if the usage of SLAM methods in real-time applications has been a long-time concern [28,29], the exploitation of GPUs to speed up perception and sensor processing [30] is less frequent in the literature. It is instead more common to see it paired with computer vision primitives or straight-up deep learning methods [31][32][33].…”