Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation

TL;DR

Multicam-SLAM introduces a multi-camera SLAM system that improves robustness and accuracy by integrating data from multiple RGB-D cameras without overlapping views, enabling better localization and mapping in complex environments.

Contribution

It presents a novel multi-camera model, multi-keyframes structure, and parallel SLAM threads for non-overlapping multi-camera visual SLAM, enhancing performance over traditional single-camera systems.

Findings

Demonstrates superior accuracy over single-camera SLAM

Shows increased robustness in challenging environments

Validates effectiveness through extensive experiments

Abstract

This paper presents a novel approach to visual simultaneous localization and mapping (SLAM) using multiple RGB-D cameras. The proposed method, Multicam-SLAM, significantly enhances the robustness and accuracy of SLAM systems by capturing more comprehensive spatial information from various perspectives. This method enables the accurate determination of pose relationships among multiple cameras without the need for overlapping fields of view. The proposed Muticam-SLAM includes a unique multi-camera model, a multi-keyframes structure, and several parallel SLAM threads. The multi-camera model allows for the integration of data from multiple cameras, while the multi-keyframes and parallel SLAM threads ensure efficient and accurate pose estimation and mapping. Extensive experiments in various environments demonstrate the superior accuracy and robustness of the proposed method compared to conventional single-camera SLAM systems. The results highlight the potential of the proposed Multicam-SLAM for more complex and challenging applications. Code is available at \url{https://github.com/AlterPang/Multi_ORB_SLAM}.