Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM
نویسندگان
چکیده
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 visualinertial 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.
منابع مشابه
Asynchronous Adaptive Conditioning for Visual-Inertial SLAM
This paper is concerned with real-time monocular visual inertial simultaneous localization and mapping (VI-SLAM). In particular a tightly coupled nonlinear-optimization based solution that can match the global optimal result in real time is proposed. The methodology is motivated by the requirement to produce a scale-correct visual map, in an optimization framework that is able to incorporate re...
متن کاملMap-merging in Multi-robot Simultaneous Localization and Mapping Process Using Two Heterogeneous Ground Robots
In this article, a fast and reliable map-merging algorithm is proposed to produce a global two dimensional map of an indoor environment in a multi-robot simultaneous localization and mapping (SLAM) process. In SLAM process, to find its way in this environment, a robot should be able to determine its position relative to a map formed from its observations. To solve this complex problem, simultan...
متن کاملAccurate Monocular Visual-inertial SLAM using a Map-assisted EKF Approach
In this paper, we present a novel tightly-coupled monocular visual-inertial Simultaneous Localization and Mapping algorithm following an inertial assisted Kalman Filter and reusing the estimated 3D map. By leveraging an inertial assisted Kalman Filter, we achieve an efficient motion tracking bearing fast dynamic movement in the front-end. To enable place recognition and reduce the trajectory es...
متن کاملGSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion
Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank1 matrix factorization technique which is more robust to the errors...
متن کاملA Distributed Framework for Monocular Visual SLAM
In Distributed Simultaneous Localization and Mapping (SLAM), multiple agents generate a global map of the environment while each performing its local SLAM operation. One of the main challenges is to identify overlapping maps, especially when agents do not know their relative starting positions. In this paper we are introducing a distributed framework which uses an appearance based method to ide...
متن کامل