版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/wangbaodong070411209/article/details/86551518
简介
VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension of VINS-Mono, which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + IMU, even stereo cameras only). We also show a toy example of fusing VINS with GPS.
特征
multiple sensors support (stereo cameras / mono camera+IMU / stereo cameras+IMU)
online spatial calibration (transformation between camera and IMU)
online temporal calibration (time offset between camera and IMU)
visual loop closure
参考论文
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors, Tong Qin, Jie Pan, Shaozu Cao, Shaojie Shen, aiXiv
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors, Tong Qin, Shaozu Cao, Jie Pan, Shaojie Shen, aiXiv
Online Temporal Calibration for Monocular Visual-Inertial Systems, Tong Qin, Shaojie Shen, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, 2018)
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator, Tong Qin, Peiliang Li, Shaojie Shen, IEEE Transactions on Robotics
解读
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