Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM
This addresses drift correction and map reuse for robotics and autonomous systems, but it is incremental as it builds on existing VINS-Mono with added relocalization and optimization features.
The paper tackles drift and lack of absolute pose estimation in monocular visual-inertial SLAM by proposing a system that relocalizes the camera, performs 4-DOF pose graph optimization for global consistency, and enables map merging, validated on public datasets with comparisons to state-of-the-art algorithms.
The monocular visual-inertial system (VINS), which consists one camera and one low-cost inertial measurement unit (IMU), is a popular approach to achieve accurate 6-DOF state estimation. However, such locally accurate visual-inertial odometry is prone to drift and cannot provide absolute pose estimation. Leveraging history information to relocalize and correct drift has become a hot topic. In this paper, we propose a monocular visual-inertial SLAM system, which can relocalize camera and get the absolute pose in a previous-built map. Then 4-DOF pose graph optimization is performed to correct drifts and achieve global consistent. The 4-DOF contains x, y, z, and yaw angle, which is the actual drifted direction in the visual-inertial system. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. Current map and previous map can be merged together by the global pose graph optimization. We validate the accuracy of our system on public datasets and compare against other state-of-the-art algorithms. We also evaluate the map merging ability of our system in the large-scale outdoor environment. The source code of map reuse is integrated into our public code, VINS-Mono.