  Multi-camera SLAM systems offer a plethora of advantages, primarily stemming
from their capacity to amalgamate information from a broader field of view,
thereby resulting in heightened robustness and improved localization accuracy.
In this research, we present a significant extension and refinement of the
state-of-the-art stereo SLAM system, known as ORB-SLAM2, with the objective of
attaining even higher precision. To accomplish this objective, we commence by
mapping measurements from all cameras onto a virtual camera termed
BundledFrame. This virtual camera is meticulously engineered to seamlessly
adapt to multi-camera configurations, facilitating the effective fusion of data
captured from multiple cameras. Additionally, we harness extrinsic parameters
in the bundle adjustment (BA) process to achieve precise trajectory
estimation.Furthermore, we conduct an extensive analysis of the role of bundle
adjustment (BA) in the context of multi-camera scenarios, delving into its
impact on tracking, local mapping, and global optimization. Our experimental
evaluation entails comprehensive comparisons between ground truth data and the
state-of-the-art SLAM system. To rigorously assess the system's performance, we
utilize the EuRoC datasets. The consistent results of our evaluations
demonstrate the superior accuracy of our system in comparison to existing
approaches.
