哈尔滨工业大学工学硕士学位论文
Abstract
SLAM (simultaneous localization and mapping) is called immediate positioning and
map reconstruction. With the rapid development of robotics, computer vision and other
fields, higher requirements have been put forward for environmental awareness
technologies. As an important component of environmental awareness, SLAM has always
been a research hotspot in robotics. The most mature technology in the field of SLAM is
laser radar. Based on the principle of laser ranging, it can obtain point clouds, position
and pose estimations, and map creation. With the increasing demand for image feature
accuracy and algorithm real-time and accuracy in many fields, the research of visual-
based VSLAM technology has become the focus of research at home and abroad. This
topic proposes a VI-SLAM algorithm based on monocular vision and inertial pre-
integration, and implements real-time VI-SLAM algorithm for transplanted embedded
devices using parallel processing.
In order to solve the problems of monocular visual feature points, frame loss, and
scale drift, a visual and IMU data fusion method is proposed. The monocular vision front
end is tracked by the optical flow method, and the feature points are simple corner
detections. After matching, the eight-point method is used for calculation. The IMU uses
the pre-integration algorithm to obtain the angular velocity and acceleration integration
results of the gyroscope and accelerometer output, and solves the problem of the
cumulative error in the world coordinate system during the integration process. The back-
end uses nonlinear optimization to obtain the optimal pose estimation, and the global map
adds relocation and loopback detection functions to optimize the pose estimation of the
mobile robot in the global map.
For the real-time requirements of embedded computing, GPU parallel processing
multi-thread operations are used to estimate the depth of image feature points in each
frame. For feature points in key frames, multi-threads sample different depths, obtain
virtual planes with several depth values, and then obtain the cost blocks integrating all
depths after back projection, and estimate the depth by optimizing the global energy
function. Local depth images are blended using TSDF to provide a global dense map that
is used directly for trajectory planning. Using the NVIDIA CUDA parallel processing
computing framework, assigning tasks such as image frame depth estimation to large
amounts of computation, and migrating embedded device NVIDIA TX1 operation VI-
SLAM algorithm.
In order to verify the performance of embedded parallel processing VI-SLAM
algorithm, relevant experiments were designed to verify. In the embedded computing
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