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Hardware Accelerator of Bundle Adjustment Algorithm

Wang, Yichen LU and Zhang, Yuzhe LU (2023) EITM02 20231
Department of Electrical and Information Technology
Abstract
With the popularization and development of CV technology, the SLAM algorithm
is widely used in scenarios such as self-driving cars and autonomous navigation
robots. As a key step in the SLAM system, the BA algorithm is responsible for
optimizing camera parameters and 3D point coordinates. BA obtains more accurate
estimates by shrinking the re-projection error. So as to support the SLAM system in
building a more accurate 3D model of the surrounding environment and a more
reliable trajectory of the moving camera. However, due to the high computational
complexity of the BA algorithm, its computational efficiency becomes a bottleneck
limiting the real-time performance of SLAM. In order to improve the performance
of the BA... (More)
With the popularization and development of CV technology, the SLAM algorithm
is widely used in scenarios such as self-driving cars and autonomous navigation
robots. As a key step in the SLAM system, the BA algorithm is responsible for
optimizing camera parameters and 3D point coordinates. BA obtains more accurate
estimates by shrinking the re-projection error. So as to support the SLAM system in
building a more accurate 3D model of the surrounding environment and a more
reliable trajectory of the moving camera. However, due to the high computational
complexity of the BA algorithm, its computational efficiency becomes a bottleneck
limiting the real-time performance of SLAM. In order to improve the performance
of the BA algorithm in practical applications, the goal of our thesis work is to build
and implement an efficient hardware accelerator for BA.
The main tasks are as follows:
• Theoretical Understanding: To fully understand the theory and ideas of
the BA algorithm, do a comprehensive review of the related literature. For
hardware implementation, this understanding provides a strong basis.
• High-Level Architecture: Create a high-level architecture with an emphasis
on the CC and JU components. This work offers a well-organized framework
for the BA algorithm and points out the parts that affect performance.
• Hardware Implementation: Create specialized hardware accelerators by translating
the high-level architecture into particular hardware designs. The hardware accelerator
needs to effectively process the JU and CC components of the BA algorithm. We aim
to focus on the performance of the BA accelerator. (Less)
Please use this url to cite or link to this publication:
author
Wang, Yichen LU and Zhang, Yuzhe LU
supervisor
organization
course
EITM02 20231
year
type
H2 - Master's Degree (Two Years)
subject
report number
LU/LTH-EIT 2023-956
language
English
id
9140483
date added to LUP
2023-11-15 11:47:42
date last changed
2023-11-15 11:47:42
@misc{9140483,
  abstract     = {{With the popularization and development of CV technology, the SLAM algorithm 
is widely used in scenarios such as self-driving cars and autonomous navigation 
robots. As a key step in the SLAM system, the BA algorithm is responsible for 
optimizing camera parameters and 3D point coordinates. BA obtains more accurate 
estimates by shrinking the re-projection error. So as to support the SLAM system in 
building a more accurate 3D model of the surrounding environment and a more 
reliable trajectory of the moving camera. However, due to the high computational 
complexity of the BA algorithm, its computational efficiency becomes a bottleneck 
limiting the real-time performance of SLAM. In order to improve the performance 
of the BA algorithm in practical applications, the goal of our thesis work is to build 
and implement an efficient hardware accelerator for BA.
The main tasks are as follows:
• Theoretical Understanding: To fully understand the theory and ideas of
the BA algorithm, do a comprehensive review of the related literature. For
hardware implementation, this understanding provides a strong basis.
• High-Level Architecture: Create a high-level architecture with an emphasis
on the CC and JU components. This work offers a well-organized framework
for the BA algorithm and points out the parts that affect performance.
• Hardware Implementation: Create specialized hardware accelerators by translating 
the high-level architecture into particular hardware designs. The hardware accelerator 
needs to effectively process the JU and CC components of the BA algorithm. We aim 
to focus on the performance of the BA accelerator.}},
  author       = {{Wang, Yichen and Zhang, Yuzhe}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Hardware Accelerator of Bundle Adjustment Algorithm}},
  year         = {{2023}},
}