PARALLELIZATION OF MONOCULAR LSD-SLAM
ALGORITHM BASED ON CUDA
ABSTRACT
In order to ensure the safety of robots or unmanned vehicles when they are in obstacle
avoidance, in addition to requiring a high calculation accuracy of the SLAM algorithm, it also
needs a smaller time complexity in order to be able to provide corresponding data to the
decision control system in time. The LSD-SLAM algorithm is a successful application of the
monocular direct method in the field of SLAM, it does not rely on feature and can build
semi-dense maps for navigation. GPU has powerful computing ability and memory bandwidth,
it is good at handing intensive and parallel data. CUDA is a GPU-based general parallel
computing architecture from NVIDIA, which can greatly improve program performance.
Although the LSD-SLAM algorithm can run on current mainstream CPUs, there is a problem
that the LSD-SLAM algorithm is easy to loss track when the camera is moving fast, and the
real-time performance of the overall algorithm is not high. To solve this problem, this paper
designs and implements accelerated camera tracking program and accelerated depth
estimation program of LSD-SLAM algorithm based on CUDA. The main research contents
and conclusions are as follows:
(1) The parallelism of the overall LSD-SLAM algorithm is analyzed. The camera
tracking module and the depth estimation module in the algorithm are taken as the specific
objects of this paper. Because the global map optimization module in the algorithm is based
on the pose graph-optimization theory and is solved by using the g2o, the calculation process
has strict fore-and-aft dependencies and cannot be parallelized. Although the optimization
calculations of the camera tracking module and the depth estimation module also have a
fore-and-aft dependence, but for each pixel in each frame of the image to do a series of
complex calculations, it is very suitable for optimization using data parallel method.
(2) A parallel camera tracking program based on CUDA acceleration was designed and
implemented. In this program, graying image and getting the pixels with significant gradients
in the camera tracking are transformed into kernel functions and executed on GPU; By doing
different block size tests on the image,
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