'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Monocular 3D reconstruction is a challenging computer
vision task that becomes even more stimulating when we
aim at real-time performance. One way to obtain 3D reconstruction
maps is through the use of Simultaneous Localization
and Mapping (SLAM), a recurrent engineering problem, mainly
in the area of robotics. It consists of building and updating a
consistent map of the unknown environment and, simultaneously,
saving the pose of the robot, or the camera, at every given time
instant. A variety of algorithms has been proposed to address
this problem, namely the Large Scale Direct Monocular SLAM
(LSD-SLAM), ORB-SLAM, Direct Sparse Odometry (DSO) or
Parallel Tracking and Mapping (PTAM), among others. However,
despite the fact that these algorithms provide good results, they
are computationally intensive.
Hence, in this paper, we propose a modified version of DSO
SLAM, which implements code parallelization techniques using
OpenMP, an API for introducing parallelism in C, C++ and
Fortran programs, that supports multi-platform shared memory
multi-processing programming. With this approach we propose
multiple directive-based code modifications, in order to make the
SLAM algorithm execute considerably faster. The performance
of the proposed solution was evaluated on standard datasets and
provides speedups above 40% without significant extra parallel
programming effort.info:eu-repo/semantics/publishedVersio