Abstract
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 this http URL 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.
Abstract (translated)
多摄像头SLAM系统具有诸多优势，主要源于其能够整合更广泛视角下的信息，从而实现更高的稳健性和更准确的局部定位精度。在本文中，我们提出了对最先进的立体SLAM系统ORB-SLAM2的重大拓展和改进，旨在实现更高的性能，我们在开始时将来自所有摄像头的测量结果映射到一个虚拟相机，称之为BundledFrame。这个虚拟相机精心设计，以无缝适应多种摄像机配置，促进多个摄像头捕获到的数据的有效融合。此外，我们还利用 bundle adjustment（BA）过程中提取的外部参数来实现精确的运动轨迹估计。 此外，我们对多摄像头场景中 bundle adjustment（BA）的作用进行了深入分析，探讨了其对跟踪、局部建模和全局优化的影响。我们的实验评估是对真实数据和最先进的SLAM系统之间的全面比较。为了严谨评估系统的性能，我们使用了EuRoC数据集。评估结果表明，我们的系统的性能优于现有方法。
URL
https://arxiv.org/abs/2403.19886
